51. 2.9.2.1. Definition of Dominant Aggregate Size Range (DASR). ...... CFA/FFA Ratio between Coarse Portion and Fine Portion of Fine Aggregates. CL. Air voids from ...... with the description of the ranges and symbol suggested. Accordingly ...
Investigating Links between Permeability and Aggregate Packing Principles for Asphalt Mixes
by Ané Betsie Marika Cromhout
Thesis presented in partial fulfillment of the requirement for the degree Master of Engineering in the Faculty of Engineering at Stellenbosch University
Department of Civil Engineering Stellenbosch University Private Bag X1 Matieland 7602 South Africa
Supervisor: Prof. Kim Jenkins Co-Supervisor: Dr. Emile Horak
December 2017
Declaration By submitting this thesis/dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch Univers ity will not infringe any third-party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.
Signature: Ané B.M. Cromhout Student Number: 18124844 Date:
Copyright Copyright © 2017 Stellenbosch University All rights reserved
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Summary This thesis deals with the investigation of permeability control of asphalt mixes. Aggregate grading and volumetric factors for asphalt mix design using different aggregate packing models are generally utilised in support of the normal asphalt mix design procedures to design for performance criteria, such as rut- and fatigue resistance. The Bailey method can be used to support the asphalt mix design in the event that the aggregate skeleton is intended to bear the biggest part of the load and to avoid deformation and rutting in the mix. This is especially true for South African conditions where thin asphalt layers of 40mm are prevalent. Other mix design procedures address the volumetric packing aspect through Voids in the Mix Aggregate (VMA) control, but may be too insensitive with regards to designing for rut resistance. Few to none of the asphalt mix design methods directly deal with permeability in the management of the aggregate skeleton design. Literature shows that at best, permeability can be managed by a number of intermediate skeleton aggregate fractions, expressed in terms of ranges of their passing a certain sieve or retained aggregate fractions. Extensive research proved that initially only the Coarse Aggregate (CA) ratio, based on the Bailey method, could be identified as an indirect control factor linked to permeability. More recent developments reconcile the simplified Dominant Aggregate Size Range (DASR) formula with the Bailey method ratios. At the outset, proving only the CA ratio adhered the numerator and denominator being separate, but also being contiguous aggregate size fractions, which in turn enables the determination of the porosity of a Bailey ratio such as the CA ratio. With the publication of new, reworked Bailey ratios, the porosity of coarse and fine portions could be determined, providing porosity ranges for the rational Bailey ratios. The physical aggregate packing in asphalt mixes and the understanding of how voids are fill ed between various contiguous aggregate fractions in the mix, shows that it resembles binary aggregate packing. The binary aggregate packing triangle is proven to show how the combined aggregate fraction gradings can become- or remain porous in two ways, namely: i) the wall effect, and ii) the loosening effect. This same triangle is proven to be used to monitor porosity of binary aggregate combinations, but also indirectly, the probability of interconnected voids from occurring. The Nominal Maximum Particle Size (NMPS) and lift thickness is proven to correlate very well with voids in the mix. Aggregate mixes with specific NMPS ranges will have a tendency to have a threshold void values in the mix value. An increase in voids above this threshold value, generally tend to show an exponential increase in permeability. In binary aggregate combinations, the wall effect and
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loosening effect seems to be the most logical physical explanations why the probability for void interconnectedness is increased. The requirement for permeability is for voids to be interconnected, because permeability is defined here as the flow of water through an asphalt layer enabled by voids that are connected to each other. This implies that permeability is not only influenced by void content alone. It is a matter of whether these voids are connected that actually influences permeability. Recent research indicates that void content determination also tends to be less accurate than desired and add to the significant scatter in data when actual permeability in the field or laboratory is determined. The physical measurement of permeability is therefore concerning, be it on site, or in the laboratory. Horizontal permeability is between 3 and 30 times more than permeability in the vertical direction. In the analysis of the data for this thesis it was found that the Marvil measurements were single measurements, which means there are inevitable false high values associated with initial absorption of water. This clearly had an influence on the quality of the data procured. The data for two Uniform Sections (US2 and US3) were analysed. One of the sets (US3) is of a section of road indicating permeability problems. Through a specialist investigation it was determined that the newly constructed surfacing is permeable. The mix design, among other parameters, was the same for the two sections and it was concluded that it was most likely due to a compaction and temperature control problem. The data analysis followed a tiered approach. The first tier was to determine to what extent the midilevel percentage passing and -retained of specific fractions could indicate a potential difference between US2 and US3 data sets. The analysis proved that porosity values of rational Bailey ratios of the finer portion of the grading tend to correlate better with actual permeability potential. It appeared that US3 could be more prone to have isolated spots with high probability of interconnected voids and therefore higher permeability. The results were not specific to an exact position and was used as a guide towards further analysis. The secondary tier entailed calculating the midi-level mass ratios (Pluggers/Interceptors size ranges); midi-range rational Bailey ratios F c/Cf and F/C; and their inverse to determine coarse over fine mass ratios. The trend that could be discerned was that the probability of high porosity, and therefore high permeability, tends to coincide with peaks in calculated rational Bailey ratio porosity values. It is clear that the spikes on the graphic representation of the values on US2 is much lower and evened out compared to US3. This provides high level confirmation that there is a significant difference observable between the two different performing data sets, with US3 confirmed to be more permeable.
iii
A third tier detailed analysis was done where the above mentioned product obtained from the rational Bailey ratios were specifically linked to the available Marvil field permeability values. The trend lines determined confirmed anticipated trends, but showed very low coefficients of correlation. The noise in the data is explained in the text, but is regardless accepted based on judgements drawn from supporting literature. The literature study extensively investigates previous published work and suggested control measures to monitor or measure asphalt permeability. The analyses in this thesis are not done on ideal datasets, but has clear merit and distinctly improved on existing control and monitoring methods. Using the methods of analysis discussed in this thesis, the performance of a pavement regarding permeability can be explained using this application of the product ratio of mass ratio and porosity. It does depend on the simultaneous calculation and comparison of at least two datasets. In this applicati on, one dataset was linked to a highway section (US2) with notable better performance in comparison to the second highway section (US3).
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Acknowledgements There are a number of people who have been fundamental in the shaping of my career and have been catalysts in my life. I would like to thank them and dedicate the hard work that went into this project to them: My father, Dr Emile Horak. I have danced with the idea of “giving up” far more times than I’d like to admit. My father has, through some inexplicable form of mental gymnastics, managed to get me away from teetering on that edge on numerous occasions. Thank you for always believing in me and steering me to better things. My mother, Mariana Horak. You’ve always been my person to bounce all my ideas off of. You’ve been the recipient of most of my bad days. I don’t know how to survive without you. A simple thanks will never be enough to convey my gratitude and love. My daughter, Emilia. Your cataclysmic presence in my life has been the wildest rollercoaster. I hope that you one day see this and know that I tried so hard to be someone you can be proud of. Thank you for your all-consuming love, tea parties on the kitchen floor, and magical ability to dissolve stress and fear with a giggle and a smile. My husband, and the rest of my family, who were all so invested in this adventure. Thank you for every break, be it tea-, coffee-, or wine-. Thanks for allowing me to explain complex concepts badly, until at least some of us understood. Prof. Kim Jenkins, who took a chance on me and allowed me to prove my merit. I am forever grateful for the opportunity to be part of this post graduate program. Thank you for always being an excellent listener and phrasing my questions that they somehow sound intelligent. My employer, JG Afrika. Without their support and backing, I would not have been able to complete this thesis. They were always accommodating when I needed time to work on my post graduate projects. They volunteered their extensive datasets on sections pron e to permeability problems, allowing me to investigate the matter. I would like to make special mention of my manager, Dr. Elsabé van Aswegen. She is my mentor and someone I strive to emulate in my professional career. Together with Paul Olivier (MD), I cannot ask for better expertise, guidance and mentorship. Thank you for always having open doors and for your valuable feedback.
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Table of Contents Contents Declaration ................................................................................................................................... i Copyright ...................................................................................................................................... i Summary ..................................................................................................................................... ii Acknowledgements ...................................................................................................................... v Table of Contents ........................................................................................................................ vi List of Tables ................................................................................................................................ x List of Figures ............................................................................................................................. xii List of Acronyms ........................................................................................................................ xvi 1.
2.
Introduction.......................................................................................................................... 1 1.1.
Problem Statement....................................................................................................... 2
1.2.
Objectives .................................................................................................................... 2
1.3.
Limitations of the Research ........................................................................................... 3
1.4.
Report Layout............................................................................................................... 3
Literature Review .................................................................................................................. 5 2.1.
Overview of Permeability of Asphalt Mixes .................................................................... 5
2.1.1.
Introduction............................................................................................................. 5
2.1.2.
Durability Impact of Permeability .............................................................................. 5
2.1.3.
The Concepts of Permeability and Porosity ................................................................ 6
2.2.
Theoretical Basis of Permeability ................................................................................... 7
2.3.
Problems with Normal Permeability Measurements ..................................................... 12
2.4.
Factors Affecting Permeability ..................................................................................... 14
2.5.
Universal permeability relationship based on voids content .......................................... 19
2.6.
Directional Permeability.............................................................................................. 23
2.7.
Conventional Aggregate Blending Methods.................................................................. 26
2.7.1.
Maximum Density Line............................................................................................ 26
2.7.2.
The Stone on Stone Contact Method ....................................................................... 32 vi
2.7.3. 2.8.
The Power Law Method for Aggregate Evaluation .................................................... 33 Bailey Method of Gradation Selection.......................................................................... 34
2.8.1.
Background............................................................................................................ 34
2.8.2.
Basic Principles....................................................................................................... 36
2.8.3.
The Volume-Density Ratios for Coarse Aggregate ..................................................... 39
2.8.4.
Control Sieve Sizes.................................................................................................. 41
2.8.5.
Aggregate Ratios .................................................................................................... 43
2.8.6.
New Bailey Ratios ................................................................................................... 46
2.8.7. Effects on VMA in the Bailey ratios .............................................................................. 48 2.9.
Methods Using the Porosity as Criteria ........................................................................ 50
2.9.1.
Porosity of Granular and Soil Mediums .................................................................... 50
2.9.2.
Porosity Principles Applied to Asphalt Mixtures ........................................................ 51
2.9.2.1.
Definition of Dominant Aggregate Size Range (DASR)........................................ 51
2.9.2.2.
DASR Porosity................................................................................................. 52
2.9.3.
Effect of Grading Type on the Ideal DASR Model ...................................................... 54
2.9.4.
Variance in Ratio of Contiguous Fractions on Porosity............................................... 55
2.9.5.
Interaction Diagrams and DASR Porosity .................................................................. 57
2.9.6.
The DASR - IC Model Characteristics ........................................................................ 59
2.10.
2.9.6.1.
Interstitial Component (IC) of Mixture Gradation.............................................. 59
2.9.6.2.
Disruption Factor (DF) ..................................................................................... 59
2.9.6.3.
Ratio between Coarse Portion and Fine Portion of Fine Aggregates (CFA/FFA) ... 60
Linking Bailey Ratios with Porosity and Permeability Control ......................................... 62
2.10.1.
Introduction....................................................................................................... 62
2.10.2.
Bailey Ratios Revisited ........................................................................................ 64
2.10.3.
Permeability Control via Bailey Ratios .................................................................. 66
2.10.4.
Concept of Rational Bailey Ratios......................................................................... 68
2.10.5.
Permeability Control via Rational Bailey Ratios ..................................................... 70
2.10.6.
Binary Aggregate Packing Linkage to Permeability ................................................ 73 vii
2.10.7. 3.
Aligning Rational Bailey Ratios with the Binary Aggregate Packing Triangle ............ 77
Evaluation Sections: Data Sources ........................................................................................ 84 3.1.
Introduction............................................................................................................... 85
3.1.1.
General Description................................................................................................ 85
3.1.2.
Roadworks ............................................................................................................. 85
3.2.
Design History ............................................................................................................ 86
3.2.1.
Background............................................................................................................ 86
3.2.2.
Aggregate quality ................................................................................................... 86
3.2.3.
Donkerhoek Quarry ................................................................................................ 86
3.2.4.
Afrisam Ferro Quarry .............................................................................................. 87
3.2.5.
Selected Sources .................................................................................................... 87
3.2.6.
Binder Quality ........................................................................................................ 87
3.2.7.
Mix Design ............................................................................................................. 87
3.2.8.
Plant trials and approved working mix designs ......................................................... 88
3.3.
Manifestation of Present Failure.................................................................................. 89
3.3.1.
Defect Manifestation and Timeline .......................................................................... 89
3.3.2.
Chronology of investigations ................................................................................... 94
3.4.
Detailed Investigations................................................................................................ 95
3.4.1.
Additional testing ................................................................................................... 95
3.4.1.1.
Test pit at N1-23 km 19.3 Northbound (30 March 2016) ................................... 95
3.4.1.2.
Test pit at N1-23 km 22.3 Northbound (31 March 2016 & 19 April 2016) ........... 96
3.4.1.3.
Hypothesis Based on Test Pit Observations ...................................................... 97
3.4.1.4.
Testing schedule (25 May 2016 to 2 June 2016) ................................................ 98
3.4.2.
Review of Acceptance Test Results (Asbuilt Data) ....................................................102
3.4.2.1.
Grading of ACM mix design ............................................................................103
3.4.2.2.
Statistical analysis for acceptance of density results ........................................103
3.5.
Remedial Actions to Date ...........................................................................................110
3.6.
Summary and Way Forward .......................................................................................114 viii
4.
Analysis of Available Data on Uniform Sections 2 and 3 on the N1 Section 22 and 23 .............115 4.1.
Introduction..............................................................................................................115
4.2.
Preparation of Data Sets ............................................................................................117
4.3.
Analysis of Data Sets..................................................................................................118
4.3.1.
First Order Data Analysis........................................................................................119
4.3.2.
Second Order Benchmark Analysis of FA mf Linked Factors ........................................132
4.3.2.1.
Benchmark Approach.....................................................................................132
4.3.2.2.
Void Interconnectedness Facilitation by Aggregate Midi-Level Skeleton Coarse
Aggregate Ratios ............................................................................................................136 4.3.2.3.
Void Interconnectedness Facilitation by Aggregate Midi-Level Skeleton Coarse
Aggregate Ratios and Binder Filler Ratio...........................................................................139 4.3.3. 5.
Tertiary Order Permeability Analysis .......................................................................141
Conclusions........................................................................................................................144 5.1.
Key Findings from Literature ......................................................................................144
5.2.
New Developments - Bailey Method for Permeability Evaluation..................................147
5.3.
Evaluation and Validation of New Permeability Parameters using N1 data ....................147
5.4.
Final Outcomes .........................................................................................................150
6.
Recommendations for Further Research ..............................................................................151
7.
References.........................................................................................................................152
ix
List of Tables Table 2. 1: Permeability coefficient (hydraulic conductivity) (x 10 -5) (Norambuena-Contreras, et al., 2013) ......................................................................................................................................... 20 Table 2. 2: Statistical results for various asphalt mixtures (Norambuena-Contreras, et al., 2013) ..... 23 Table 2. 3: Typical factors for Bailey Coarse and SMA Graded Mixture Control Sieves (Aurilio, et al., 2005) ......................................................................................................................................... 42 Table 2. 4: Bailey Fine Graded Mixture Control Sieves (Aurilio, et al., 2005).................................... 43 Table 2. 5: Aggregate Ratios for Coarse and SMA Graded Mixtures (Zaniewski & Mason, 2006) ....... 45 Table 2. 6: Aggregate Ratios for Fine Graded Mixtures (Zaniewski & Mason, 2006) ......................... 45 Table 2. 7: Recommended Ranges of Aggregate Ratios for Coarse and Fine Mixtures (Zaniewski & Mason, 2006) ............................................................................................................................. 46 Table 2. 8: Recommended Ranges of Aggregate Ratios for SMA Mixtures (Zaniewski & Mason, 2006) .................................................................................................................................................. 46 Table 2. 9: Effects of Increasing Bailey Parameters on VMA (Vavrik, 2002) ..................................... 48 Table 2. 10: Change in value of Bailey Parameters to produce 1% change in VMA .......................... 49 Table 2. 11: DASR porosity formulas (Horak, et al., 2017a) ............................................................ 63 Table 2. 12: Existing Bailey ratios description (Horak, et al., 2017a) ............................................... 65 Table 2. 13: Suggested permeability control ranges and ratios (Horak, et al., 2017a) ...................... 67 Table 2. 14: Rational Bailey ratios with good correlation with DASR porosity parameters (Horak et al, 2017 a, b & c) ............................................................................................................................. 69 Table 2. 15: Average porosity values of aggregate fraction ranges for reworked data sets for low and high permeability mixes (Horak et al (2017a)* .............................................................................. 71 Table 2. 16: Average Bailey ratios for reworked data sets for low and high permeability mixes (Horak et al, 2017a)* ............................................................................................................................. 71 Table 2. 17: Suggested permeability control criteria for Bailey ratios and DASR fraction porosity ranges*...................................................................................................................................... 72 Table 2. 18: Rational and revised Bailey ratios with good correlation with DASR porosity parameters* .................................................................................................................................................. 79
Table 3. 1: Summary of specialised testing ................................................................................... 88 Table 3. 2: Summary of control parameters for each mix design .................................................... 88 Table 3. 3: Manifestation of defects and timeline ......................................................................... 89
x
Table 3. 4: Comparison between levels of compaction of individual results on N1-23-2013/RH/2 (USIII) and N1-22-2011/RH (USII) for the 40mm surfacing layer (ACM)....................................................104 Table 3. 5: BPCC N1-23-2013/RH/2 Northbound – reported versus recalculated acceptance control for 40mm ACM ...............................................................................................................................108 Table 3. 6: BPCC N1-23-2013/RH/2 Southbound – reported versus recalculated acceptance control for 40mm ACM ...............................................................................................................................109 Table 3. 7: Summary of average level of compaction achieved on US3 and US2 for 40 mm ACM ....110 Table 3. 8: Benchmarking Marvil Permeability test results carried out on the 40 mm asphalt surfacing after the 1st and 2nd fog spray ...................................................................................................112
Table 4.1: Suggested permeability control ranges and ratios ........................................................119 Table 4.2: Suggested permeability control criteria for Bailey ratios ranges (Horak e t al, 2017a&b)..120 Table 4.3: Suggested permeability control criteria for DASR fraction porosity ranges (Horak et al, 2017a&b) ..................................................................................................................................121 Table 4.4: Average porosity and mass proportions of FA mf for US2 and US3 ..................................122 Table 4.5: Result of t-test for combined product of porosity and mass proportions of FA mf for US2 and US3...........................................................................................................................................122 Table 4.6: Result of t-test for coarse proportion in FA rcm ratio for US2 and US3..............................128 Table 4. 7: Benchmark ranges for FA mf related porosity, ratios and product...................................132
xi
List of Figures Figure 2. 1: Schematic relationship between hydraulic conductivity and air void content in asphalt mixtures with different pore structures (Norambuena-Contreras, et al., 2013) ................................. 9 Figure 2. 2: CoreLok Equipment (Horak, et al., 2017a) ................................................................... 11 Figure 2. 3: Comparison of Air Voids Determined Using the CoreLok and standard tests (Cooley, 2003) .................................................................................................................................................. 12 Figure 2. 4: Effect of Nominal Maximum Aggregate Size on Field Permeability (Cooley, et al. 2002) . 15 Figure 2. 5: Typical binary aggregate combination porosity influence with varying diameter ratios and proportion of coarse aggregate (Olard, 2011; Furnas, 1928) .......................................................... 16 Figure 2. 6: Schematic illustration of porosity influence due to the wall and the loosening effect (Knop & Peled, 2016) ............................................................................................................................ 16 Figure 2. 7: Influence of coarse aggregate in the binary combination on permeability (Mota, et al., 2004) ......................................................................................................................................... 17 Figure 2. 8: Method for selecting critical air void and permeability values (Cooley, 2003) ................ 18 Figure 2. 9: Visual classification or interconnectedness of voids and permeability indication (addapted from Chen et al, 2004) ................................................................................................................ 21 Figure 2. 10: Negative Logarithm of permeability of measured and literature values versus the inverse of air void content (Norambuena-Contreras, et al., 2013) ............................................................. 22 Figure 2. 11: FEM Flowlines /Pressure head (Kv/Kh= 5 and k=5x 10-3 cm/s (Harris, 2007) ................ 25 Figure 2. 12: Typical repetition and diameter of falling head permeameter effect (Harris, 2007) ..... 26 Figure 2. 13: Typical Gradation Curve with the Continuous Maximum Density Line (Goode & Lufsey, 1965) ......................................................................................................................................... 27 Figure 2. 14: SHRP specifications for Aggregate Gradations with NMAS 12.5mm (Strategi c Highway Research Program, 1994) ............................................................................................................ 28 Figure 2. 15: Four different 12.5 mm NMAS gradations inclusive of CMD (TRB (Transportation Research Board), 2011) ............................................................................................................................. 30 Figure 2. 16: % Deviation from CMD gradation for the same blends as in Figure 2. 15 (TRB (Transportation Research Board), 2011) ....................................................................................... 31 Figure 2. 17: Effect of changes in aggregate gradation on VMA as shown on a CMD plot. (TRB (Transportation Research Board), 2011) ....................................................................................... 32 Figure 2. 18: Power Law Gradation Evaluation (Ruth, et al., 2002) ................................................. 34 Figure 2. 19: The four Bailey method principles (Zaniewski & Mason, 2006). .................................. 36
xii
Figure 2. 20: Separation between Bailey Coarse and Fine Aggregate for 19mm NMPS ( Zaniewski & Mason, 2006). ............................................................................................................................ 38 Figure 2. 21: Estimation of Void Size between Aggregates for Bailey Control Sieves (Aurilio, et al., 2005). ........................................................................................................................................ 39 Figure 2. 22: Loose Versus Rodded Unit Weight Compacted Aggregate Samples (Vavrik, 2002) ....... 40 Figure 2. 23: Selection of Chosen Unit Weight for Coarse Aggregate (Vavrik, 2002) ........................ 41 Figure 2. 24: Chosen Unit Weight vs. Change in VMA (Aurilio, et al., 2005) ..................................... 49 Figure 2. 25: Relationship among soil phases (Craig, 2004) ............................................................ 50 Figure 2. 26: Schematic of DASR and IC concept for three basic grading types (Roque, et al., 2006) . 52 Figure 2. 27: Asphalt mixture components or phases (Chun, 2012). ............................................... 53 Figure 2. 28: Typical grading types versus the continuous maximum density (CMD) line (Roque, et al., 2006) ......................................................................................................................................... 54 Figure 2. 29: Porosity values for individual fractions for three distinctive gradings (Roque, et al., 2006) .................................................................................................................................................. 55 Figure 2. 30: Spacing change between particles in a hexagonal configuration for a 9.5 mm and 4.75 mm aggregate contiguous combination (Roque, et al., 2006) ........................................................ 56 Figure 2. 31: Change in spacing (Slope) for a binary (contiguous) mixture ...................................... 57 Figure 2. 32: Interaction diagram for three types of grading (Roque, et al., 2006) ........................... 58 Figure 2. 33: Porosity of individual fractions and combined as contiguous combination .................. 59 Figure 2. 34: Illustration of Disruption Factor ranges versus DASR composition (Gaurin, 2009). ....... 60 Figure 2. 35: Determination of the CFA/FFA of the IC (Chun, 2012) ................................................ 61 Figure 2. 36: Description of Bailey control sieves and aggregate ranges (Shang, 2013) .................... 64 Figure 2. 37: Illustration of various skeleton infill structures of overall aggregate matrix (Horak, et al., 2017c)........................................................................................................................................ 74 Figure 2. 38: The Furnas principles applied to a binary combination of coarse and fine aggregates (adapted from Mota et al, 2013).................................................................................................. 75 Figure 2. 39: Effect of aggregate dimension ratio and volume ratios on permeability (Horak, et al., 2017c)........................................................................................................................................ 76 Figure 2. 6: Schematic illustration of porosity influence due to the wall and the loosening effect (Horak et al, 2017c after Knop and Peled, 2016) ...................................................................................... 77 Figure 2. 41: Good performing data set reworked porosity and coarse to fine proportion ............... 81 Figure 2. 42: Poor performing data set reworked porosity and coarse to fine proportion................ 82 Figure 2. 43: SCS to TCS fraction ratio and porosity ....................................................................... 83
xiii
Figure 3. 1: Flow diagram illustrating acquisition of data, research and data analysis ...................... 84 Figure 3. 2: Extent of test pit N1-23 km 19.3 Northbound.............................................................. 95 Figure 3. 3:Depth of excavation at the yellow line at N1-23 km 19.3 Northbound........................... 96 Figure 3. 4:Moisture bubbling out of crack at 12:40, which was dry earlier the day......................... 96 Figure 3. 5: Permeability vs percentage field voids per lane, only for 40mm surfacing..................... 99 Figure 3. 6: Permeability vs percentage field core compaction per lane, only for 40mm surfacing ..100 Figure 3. 7: Permeability vs percentage field voids per lane, only for BTB (with and without RA) ....100 Figure 3.8: Permeability vs percentage field core compaction per lane, only for BTB (with and without RA) ...........................................................................................................................................101 Figure 3.9: Permeability vs percentage field voids per lane, only for BTB below Glassgrid ..............101 Figure 3. 10: Permeability vs percentage field voids per lane, only for BTB below Glassgrid............102 Figure 3. 11: Average grading comparison between US2 and US3 .................................................103
Figure 4. 1: Layout and position of Marvil Permeability measurements on US3 .............................116 Figure 4.2: All data combined for US2 and US3 product of porosity and mass ratio for FA mf rational Bailey Ratio ...............................................................................................................................123 Figure 4.3: Histogram of all data combined product of porosity and mass ratio for FA mf rational Bailey Ratio for US2 .............................................................................................................................124 Figure 4.4: Histogram of all data combined product of porosity and mass ratio for FA mf rational Bailey Ratio for US3 .............................................................................................................................124 Figure 4.5: Probability density function of all data combined product of porosity and mass ratio for FAmf for US2 ..............................................................................................................................125 Figure 4.6: Probability density function of all data combined product of porosity and mass ratio for FAmf for US3 ..............................................................................................................................126 Figure 4.7: Coarse proportion in FA rcm ratio for both data sets for all lanes combined ....................127 Figure 4.8: Probability density function of all data combined for coarse proportion in FA rcm ratio for US2...........................................................................................................................................129 Figure 4.9: Probability density function of all data combined for coarse proportion in FA rcm ratio for US3...........................................................................................................................................129 Figure 4. 10: Rational Bailey ratio Cf/Fc porosity and coarse mass proportion product....................130 Figure 4. 11: Rational Bailey ratio F/C porosity and coarse mass proportion product .....................130 Figure 4.12: US2 and US3 facilitating factors (FA rmf, Fc/Cf and C/F) mass ratio product....................131 Figure 4.13: US3 fast lane and shoulder, for SB and NB, FA mf product of porosity and mass proportion .................................................................................................................................................134 xiv
Figure 4.14: Probability density function of fast lane and shoulder FA mf Product of Porosity and Mass Proportion for US3 SB ................................................................................................................135 Figure 4.15: Probability density function of fast lane and shoulder FAmf Product of Porosity and Mass Proportion for US3 NB ...............................................................................................................135 Figure 4.16: US3 Slow lanes of NB and SB product ratio of FA rmf, Fc/Cf and C/F mass ratios.............136 Figure 4. 17: US3 Fast lanes and shoulder of NB and SB product of product ratio of FA rmf, Fc/Cf and C/F mass ratios................................................................................................................................137 Figure 4. 18: US2 Fast lanes and slow lane SB product of product ratio of FA rmf, Fc/Cf and C/F mass ratios ........................................................................................................................................138 Figure 4. 19: US3 Fast lanes and slow lane SB product of filler binder ratio with the product ratio of FArmf, Fc/Cf and C/F mass ratios...................................................................................................139 Figure 4. 20: US3 Fast lanes and slow lane NB product of filler binder ratio with the product ratio of FArmf, Fc/Cf and C/F mass ratios...................................................................................................140 Figure 4. 21: US2 Fast lanes and slow lane SB product of filler binder ratio with the product ratio of FArmf, Fc/Cf and C/F mass ratios...................................................................................................141 Figure 4. 22: US3 all lanes Marvil permeability versus derived FA mf porosity ..................................142 Figure 4. 23: US3 all lanes Marvil permeability versus derived FA mf coarse aggregate proportion ...143 Figure 4. 24: US3 all lanes Marvil permeability versus derived FA mf product of porosity and coarse aggregate proportion.................................................................................................................143
xv
List of Acronyms Some acronyms are omitted from this list because their explanation is more extensive than a couple of words. They are thoroughly explained in the text where they appear. A
Cross sectional area of specimen
aCA
Intercept constant for the coarse aggregate
ACM
Medium continuously graded asphalt surfacing
aFA
Intercept constant for the fine aggregate
Avc
Air void content
BAP
Binary aggregate packing
BPCC
Bakwena platinum corridor concessionaire (pty) ltd
BRD
Bulk relative density
BTB
Bitument treated base
C
The combination of pluggers and interceptor mid-range coarse aggregate
C/F
The combination of pluggers and interceptor mid-range coarse aggregate over all fines
CA
Coarse aggregate ratio
CC
Oversize or coarse of coarse aggregate ratio
Cf
Mid-range coarse of fine
Cf/Fc
Mid-range coarse of fine over fine of coarse, or interceptor stability ratio
CFA
The IC portion on the gradation curve between the 1.18 mm sieve and 2.36 mm sieve
CFA/FFA Ratio between Coarse Portion and Fine Portion of Fine Aggregates CL
Air voids from CoreLok machine
CMD
Continuous maximum density
CUW
Chosen unit weight
d
Sieve opening width, mm xvi
DASR
Dominant aggregate size range
DF
Disruption factor
E
Stiffness, expressed in Pa
F
All fines
F/C
All fines over the combination of pluggers and interceptor mid-range coarse aggregate
FA
Fine aggregate ratio
FAc
Coarse portion of fine aggregate ratio
FAcm
Stability of the coarse range of the fine portion
FAf
Fine Portion of the fine aggregate ratio
FAmf
Mastic control ratio, or the finer portion of the fines (without the filler component) versus the overall fines portion
FArmf
Inverse of FAmf, mass ratio
Fc
Fine of Coarse
Fc/Cf
Fine of coarse over mid-range coarse of fine
Fd
Free draining
FE
Fracture energy
FEM
Finite element method
FFA
The portion of the IC between the 1.18 mm sieve and inclusive of the 75 micron sieve
h
Pressure head
HMA
Hot mix asphalt
HS
Half Sieve or Half Size
I/P
Interceptor to Plugger ratio
IC
Interstitial components
ITS
Indirect tensile strength
IV
Interstitial volume
xvii
k
Coefficient of permeability (cm/s)
L
Length of specimen
Ln
Natural logarithm
LP
Low permeability
LUW
Loose unit weight
MFD
Very permeable or moderate free draining
Ƞ
Porosity
ηDASR
DASR porosity
NB
Northbound
nCA
Slope (exponent) for the coarse aggregate
Ndes
Design number of gyrations of the gyratory compactor
nFA
Slope (exponent) for the fine aggregates
NMAS
Nominal maximum aggregate size
NMPS
Nominal maximum particle size
P
Permeable to draining
P/O
Plugger to oversize (large) aggregate ratio
P1.18
Percent passing 1.18mm sieve
P12.5
Percent passing 12.5mm sieve
PCA
Percent by weight passing a given sieve that has an opening of width d on the coarse aggregate (CA) portion of the grading curve
PCMD
Percent passing, continuous maximum density gradation
PCS
Primary control sieve
PFA
Percent by weight passing a given sieve that has an opening of width d on the fine aggregate (FA) portion of the grading curve
PHS
Percent Passing the Half Sieve
PPCS
Percent Passing the Bailey Primary Control Sieve
xviii
PSCS
Percent Passing the Secondary Control Sieve
PTSC
Percent Passing the Bailey Tertiary Control Sieve
Q
Rate of flow (e.g. L/s)
RAG
Red amber green
RIC
Rolled-in chips
RUW
Rodded unit weight
SA
Surface areas
SB
Southbound
SCS
Secondary control sieve
SG
Specific gravity
SHRP
Strategic highways research program
SMA
Stone matrix asphalt
t
Time during which Q is measured
TCS
Tertiary control sieve
US2
Uniform section 2
US3
Uniform section 3
V
Total volume
V AGG>DASR
Volume of particles bigger than DASR
VCA
Voids in the Coarse Aggregate
VCAall
Voids in the coarse aggregate for the entire mixture
VCAca
Voids in the coarse aggregate for the coarse aggregate only fraction
VFB
Voids Filled with Bitumen
V ICAGG
Volume of IC aggregates
VLP
Very low permeability
xix
VMA
Voids in Mineral Aggregate or Voids in the Mix Aggregate
V T(DASR)
Total volume available for DASR particles
VTM
Volume of the Total Mix
Vv
Volume of Voids
V V(DASR)
Volume of voids within DASR
WMA
Warm mix asphalt
Δt
Time difference
xx
1. Introduction The focus of this literature review is to monitor or determine asphalt permeability by using various asphalt mix design procedures and methods. Normally, asphalt design methods tend to concentrate on the contribution of the aggregate matrix, or skeleton, towards rut resistance of asphalt mixes. Fatigue, as another specific main performance determinant, is not discussed here, but where relevant, mention is made of the effects related to aggregate packing or matrix characteristics regarding permeability or porosity principles. Load transfer within a typical Hot Mix Asphalt (HMA) layer is believed to be primarily facilitated via the aggregate skeleton. It is widely acknowledged that resistance to deformation and rut (measured on the surface) can largely be countered by the aggregate component (up to 90 to 95% by volume) forming an aggregate skeleton. The aggregate or stone particles have various sizes and shapes which all contribute to the integrity of this aggregate skeleton. The common understanding is the brunt of the load is generally carried by the larger aggregate which has stone on stone contact (aggregate on aggregate) with a matrix of finer aggregate and bitumen binder that provide stability for the larger aggregate skeleton and therefore enhances the resistance to rut. In some mixes it is often observed that the larger aggregates are actually pushed aside by the finer aggregate particles. Load is consequently, in this case, not primarily carried and transferred via large aggregate on aggregate (stone on stone) contact, but a combination of the midsized aggregate fraction or even finer. The South African Design and Use of Asphalt in Road Pavements, Manual 35, (SABITA, 2016) is a simplified asphalt mix design manual which intelligently incorporates the best aspects of modern design procedures such as Superpave and the older mix design procedures such as the Marshall method. It also simplifies the approach towards performance specifications. This is done by following the Eurocode approach to limit such performance specifications (such as rutting) to enable direct comparison of the different mix designs. This South African asphalt mix design manual has correctly identified this principle of aggregate (stone) skeleton as main load transfer mechanism and resisting rut. This description of stone skeleton is used as main descriptor or distinction in the definition of asphalt mix types. This SABITA Manual 35 (SABITA, 2016) is used as launching guide in this research project, as it defines that asphalt mixes are primarily classified into two main categories based on aggregate packing i.e. sand-skeleton or stone-skeleton types. In a stone skeleton mix the coarser aggregate fractions are in direct contact with each other, thus forming a coarse aggregate structural matrix. The spaces (voids) between the matrix of large aggregate (stones) are filled by the finer aggregate fractions and bitumen binder (acting as glue). The latt er two 1
components in effect are accordingly providing stability to the coarse aggregate skeleton. The finer aggregate fractions and bitumen binder should not push the coarser aggregates apart. Contact between the coarser aggregate fractions should therefore be assured. This circumstance brings about that the applied load on the layer is carried by a matrix, or skeleton, of the coarser aggregate portion. Examples include coarse continuously graded asphalt, stone mastic asphalt, ultra-thin friction courses, and open graded asphalt (porous) asphalt. In sand-skeleton mixes, the loads on the layer are mainly carried by the finer aggregate fraction, with the larger fractions providing bulk and replacing a proportion of the finer fraction. There is no meaningful contact between the individual larger aggregate particles and they do not primarily contribute to load transfer, but more like volume filler solids. Examples include semi -gap graded asphalt, gap-graded asphalt, and medium / fine continuously graded asphalt. As already alluded to, these skeletons adhere to the broad concept of aggregate packing theory. An overview of literature is given here to illustrate that even this concept of aggregate packing is often difficult to describe in other terms than what had been described mostly in empirical relationships. The intricacies of aggregate form or morphology, binder content or effective film thickness, filler size interaction as mastic, compaction efficiency and other aspects also need to be incorporated as they impact on the theory of aggregate packing. The literature review naturally culminates in an overview of the Bailey method, The Dominant Aggregate size range (DASR) and the linkage with the Binary Aggregate Packing (BAP) methodology to derive credible values of porosity which are linked to permeability.
1.1.
Problem Statement
There is a need to investigate and quantify the links between permeability and aggregate packing principles for asphalt mixes to enable true optimisation to be achieved in compositional balance. Current mix design procedures aim to design for performance criteria such as rut and fatigue resistance, but rarely directly address the important aspect of permeability, which indirectly links to durability aspects, such as stripping and ravelling.
1.2.
Objectives
The aim of this thesis is to establish, through extensive research, a method of linking permeability to aggregate packing principles. The study must provide answers to what extent these asphalt mix design procedures can be manipulated to decide structural strength aspects, but primarily, monitor or determine asphalt permeability. Furthermore, a link must be established between concepts such as permeability, porosity and the interconnectedness of voids, to aid in proving that void content alone cannot give satisfactory judgement on aspects like permeability. 2
1.3.
Limitations of the Research
The data is obtained from a consulting engineering environment. The datasets were compiled as part of a specialist investigation and therefore in the larger part, only focusses on areas where, e.g. pumping of fines, were observed, denoting permeability concerns. Ideally, comparative results denoting low permeability in adjacent sections would be supportive, but understanding that the same construction practices result in adjacent areas to most probably have permeability values in the same ranges. The only difference is that these flagged sections, or areas indicating distress, are likely marginally more permeable, creating an easier flow path, and as a consequence, exhibiting signs of permeability issues earlier in its life than adjacent “good” sections. The data obtained inlcuded noise and high variability in the data, which was possibly created by the facets emphasised by Harris (2007): Marvil permeability may be expose d to a much higher variation in horizontal permeability compared to vertical permeability due to the measurement itself. The Marvil measurements were single measurements which means there are inevitable false high values associated with initial absorption of water. This clearly had an influence on the quality of the data procured. Apart from established constraints pertaining to the Marvil and other permeability measuring techniques (Horak, et al., 2012), this study limits itself to the permeability characteristics of the surfacing layer only.
1.4.
Report Layout
This report comprises of different chapters whereof Chapter 2 (Literature Review) undertakes the amalgamation of existing research in one concise section to aid in the understanding of underlying concepts. The literature review deals with the use of aggregate grading and volumetric factors for asphalt mix design. It serves to prove that various aggregate packing models and methods to support the normal asphalt mix design procedures can be used to design for performance criteria such as rut and fatigue resistance, along with concise deductions on the permeability, porosity and probable interconnectedness of voids. The first section pertains to permeability concepts of asphalt mixes and discusses how that ties in with the concept of porosity. This section also discusses the constraints and problems frequently encountered with measuring permeability. It then aims to tie in the concept of permeability with theory on voids and how that may impact porosity, which in some cases better explains how water moves through an asphalt layer. The remainder of this chapter is dedicated to a critical review conducted of the Bailey ratios and reconciles it with the porosity principles of the DASR method by
3
limiting the Bailey ratios to only contiguous aggregate ranges in the numerator and denominator. This chapter lays the foundation for the analyses completed in later sections of this thesis. Chapter 3 discusses the source data and sites from which it originated. The interesting problems encountered on these sites created a unique opportunity to employ the concepts learned from Chapter 2 in the literature review. Chapter 4 discusses the methodology of the analysis of the datasets and statistical principles used. The analyses are presented simultaneously with the discussion on the methods employed because separating the method from the data renders the explanation to be a jumble of acronyms. In this chapter, the reader can follow the logical deductions and be aided by clear graphs that bring home the rather complex concepts. Chapter 5 extrapolates the conclusions drawn from the analyses and ties the results in with the extensive literature that either verifies, or contradicts the reasoning. Chapter 6 elaborates on the lessons learnt throughout this thesis by providing advice and recommendation for future study in this field. Chapter 7 lists the research source documents used throughout this thesis.
4
2. Literature Review 2.1.
Overview of Permeability of Asphalt Mixes
2.1.1. Introduction At intermediate to high temperatures, the majority of traffic loads are carried by an asphalt mixture’s aggregate structure. Repeated loading applications can be resisted by a strong stone skeleton. The subsequent asphalt mix’s performance is therefore largely dependent on the aggregate gradation. During construction of hot mix asphalt (HMA), the in-place density is the factor measured in quality control (QC) and quality assurance (QA) procedures of constructed asphalt layers. Density is indirectly related to HMA permeability. Khosla & Sadasivam (2006) quote other researchers in stating that permeability actually gives a better measure of the durability of HMA than density. It is understood that low densities achieved with typical HMA mixes tend to have higher percentages of air voids, but it is in reality only when these air voids are interconnected that water will flow through them and the HMA can become permeable. It is then a logical step in understanding that with higher percentages air voids the probability that these voids can be interconnected also increases (Mallick, et al., 1999). Testing by Hainin et. al. (2003) proved that permeability of normal HMA (typical nominal maximum aggregate size 12.5mm to 9.5mm) is very low when the air void contents is lower than 6 percent. Increasing the air void content to between 6 and 7 percent, results in significant increases in permeability. It has been found by various researchers that in general, asphalt pavement layers become prominently permeable with air void contents between 8 and 8.5 percent (Hainin, et al., 2003).
2.1.2. Durability Impact of Permeability Permeability of hot mix asphalt (HMA) is an aspect imperative for a durable pavement. Premature deterioration is linked to extended exposure to water within the pavement structure. Stripping is one of the main results, and likely the most devastating effect, of such penetration of water within the pavement. If the adhesive bond between aggregate and binder is broken, the result is known as stripping. Stripping is more complex than only the breaking of the adhesive bond and is instigated by many factors. That being said, a shared variable evident at all cases of stripping is moisture within the pavement (Roberts, et al., 1996). The onset of stripping is followed by various pavement distress forms. Typically, it may manifest as surface ravelling, deformation, cracking and emulsification of the binder. Therefore, when pavements are permeable the potential for stripping is enormously increased.
5
The environment need not be wet and humid in order for stripping to take place, as proven by experience on airports pavements (Horak, et al., 2012). A significant potential problem that occurs when pavements are permeable, is that the rate of oxidation of the bitumen binder is greatly increased. It stands to reason that if a particle of water can penetrate into the pavement, then so can air. Oxidation is known to cause the binder to become brittle, which can lead to mechanisms such as crack development and ravelling of the asphalt layer.
2.1.3. The Concepts of Permeability and Porosity Hydraulic conductivity is synonymous to the permeability of an asphalt pavement layer. In general terms, the permeability of a pavement layer is measured as the rate of flow of a fluid through the layer under a unit head of water. Jackson and Mahoney (2014) use the term “isotropic permeability” since permeability implies the flow of water in a specific direction, generally assumed vertical. Vertical water flow is assumed to be due to gravity, associated with the access of the water from the top and through the of the asphalt layer towards the lower support layers. (Jackson & Mahoney, 2014) Porosity is defined as the percentage of air voids per compacted hot mix asphalt (HMA) sample that is accessible to water. Jackson and Mahoney (2014) make the distinction that porosity is the ability of asphalt to absorb water while permeability is the ability to transmit the water in the asphalt layer. (Jackson & Mahoney, 2014) Mogawer et al (2002) specifies that porosity may be a better indicator of air and water infiltration as porosity is easier to measure than permeability. However, Jackson and Mahoney (2014) point out that an asphalt can be porous, but not permeable. It is also true that an asphalt can only be permeable if it is also porous. Therefore, permeability should also be measured and not just porosity due to the intersected concepts of the voids. (Mogawer, et al., 2002) Numerous factors can potentially affect the permeability of HMA pavements. Ford and McWilliams (1988), and Hudson and Davis (1965) lists the following influencing factors, based on studies on finegraded mixes: •
Gradation
•
Particle shape
•
Air void level or percentage of voids
•
Size of air voids
None of these researchers indicate how interconnectedness of voids are actually formed, not only in relation to porosity, but to enable transmission of water through the asphalt medium.
6
The introduction and use of the SuperPave method for mixes of asphalt pavements resulted in researchers finding that the designed SuperPave mixes tended to produce coarser mixes than before. Choubane et al (1998) established that coarse-graded SuperPave mixes with air void levels of 6 percent (lower than previously thought) can be overly permeable . Cooley and Brown (2000) proved this in following years and stated that regardless of whether the percentage air voids are below 8 percent, coarse-graded SuperPave mixes are likely to be markedly permeable, determined by a field permeability device, or permeameter. This was also confirmed by Hainin, et al (2003) and Mallick et al (2003). A conclusion drawn by these researchers are that there is high variability of the interconnectedness of the voids and thus directly influencing the permeability values. This aspect of “interconnectedness of voids” was not necessarily reflected by the traditional measuring methods such as determining the percentage of voids in the mix, Voids in Mineral Aggregate (VMA), etc., in the asphalt layers. It also became clear that many debatable assumptions are used to measure permeability. Even though density is directly linked to voids in the mix, it does not give a satisfying reflection of permeability correctly under all circumstances of varying mix design types, Nominal Maximum Particle Size (NMPS), binder content, etc. Awadalla et al. (2017) states that there have been numerous research works to study the permeability characteristic and ultimately to recommend a reliable test method, or acceptance permeability criteria. Other studies have also attempted to propose permeability limits to be added to the current quality assurance (QA) and quality control (QC). Despite this growing concern of HMA permeability, little efforts were devoted to understand the inter-relationship between permeability, in the field and laboratory, and other important mechanical and physical pavement characteristics. If these relationships are investigated and quantified, the possibility of incorporating the permeability within the mix design procedure will not only be highlighted and addressed, but also field permeability prediction based on laboratory procedure can be ultimately achieved (Awadalla, et al., 2017).
2.2.
Theoretical Basis of Permeability
Isotropic permeability can be measured using Darcy’s law as theoretical basis. Darcy’s law states that for laminar flow conditions, a linear relationship exists between specific discharge and the hydraulic gradient. Turbulent flow is discerned by complex flow paths which create a non-linear relationship between specific discharge and hydraulic gradient. Darcy’s law is described in the inset on the next page.
7
In spite of the limitations to Darcy’s law it is used extensively in practice to determine isotopic permeability by various test methods in the laboratory and field. There are two methods described in Darcy’s Law to calculate the coefficient of permeability: Henry Darcy established the fundamental
1) Constant water head method: the rate of
ideas of permeability in 1856 when he
flow is measured while providing a constant head
originally defined the term called the
of water. The coefficient of permeability or
coefficient of permeability (Harris, 2007).
hydraulic
This term is used to determine the fluid
represented as:
flow through a porous medium (e.g asphalt
k=(Q. L)/( h. A. t)
material). This principle is an extension of
conductivity can
therefore
be
where:
Newton’s Second Law and is applicable k = coefficient of permeability (cm/s)
when the flow of the fluid through the
Q = rate of flow (e.g. l/s)
material is laminar.
L = length of specimen Darcy’s Law can be written as: h = pressure head Q=k.i.A =k.(ΔL/L).A
A = cross sectional area of specimen
where:
t = time during which Q is measured
Q = rate of flow 2) Falling head permeability method: the head k = coefficient of permeability
loss over time is measured as the water head drops. Now, the coefficient of permeability can
i = hydraulic gradient
be represented as:
A = cross sectional area of specimen perpendicular to the direction of flow
k=(a.L/A. Δt).ln(h1/h2)
L = length of the specimen
where: k = coefficient of permeability a = area of standpipe L = length of specimen h1 = water head at beginning of test h2 = water head at end of test A = cross sectional area of specimen Δt = time between reading h1 and h2
8
Harris (2007) makes a distinction for where each method, described above, is more applicable. He notes that k values greater than 10-3 cm/s (which denotes more permeable mediums) are best suited making use of the constant head method. Less permeable mediums with k values smaller than 10-3 cm/s (like asphalt concrete with k values between 10-3 cm/s and 10-5 cm/s) are best suited to the falling head method. (Harris, 2007) The constant head method is more appropriate for laboratory conditions. The falling head method is used mostly in the field. However, water flow through a porous medium, such as some asphalt mixes, is not always following the assumptions made and is therefore not always adhering to the correct application of Darcy’s law. The complexity of water flow through various types of asphalt mixes is conceptually illustrated in Figure 2. 1. It is clear for the high void content situation, such as for porous asphalt, that Darcy’s law is closely followed based on the assumptions stated earlier. In that case the water flow is still virtually laminar and flowing in one direction. As the asphalt void content decreases from porous asphalt (case 1 in Figure 2. 1) towards the denser asphalt mixes (3 or 4 in Figure 2. 1) the flow paths become more turbulent and tortuous by twisting and turning on its way through the asphalt layer. In this dense asphalt mix case aspects such a capillary flow and hygroscopic effects plays a more significant role in the actual permeability. Tortuosity is in effect the actual length of the water path through the material versus the actual thickness of the layer. Tortuosity therefore becomes a more essential factor when void content lowers and should be incorporated in permeability determination (NorambuenaContreras, et al., 2013). Thus, there is recognition that Darcy’s law may be too simplistic as applied to all types of asphalt mixtures in all situations.
Figure 2. 1: Schematic relationship between hydraulic conductivity and air void content in asphalt mixtures with different pore structures (Norambuena-Contreras, et al., 2013)
9
A more accurate equation for hydraulic conductivity of a fluid through a packed
Al-Omari simplified Kozony-Carmen equation
bed of solids is the Kozeny-Carman equation. This equation attempts to take account of the porosity of the bed of solids, viscosity of the fluid and the sphericity of the solids as a simplified model for an asphalt layer. An alternative method was hence proposed by Al-Omari
Where: k = coefficient of permeability (m/s) n = the percent total air voids g = 9.79 kN/m3 unit weight of water at 20o C
(2004) for estimating permeability is based on a simplified version of the
c = 3 is a constant
complex Kozony-Carmen equation to
T = tortuosity (indication of the tortuous path
estimate
(twisting and turning) water follows in a porous
permeability
of
asphalt
mixtures. (Al-Omari, 2004)
medium) S = the average specific surface area of given
This simplified Al-Omari equation shows
gradation and NMPS
that
μ = 10-3 kg/(m sec) is the water viscosity
the
effective air
voids are
proportionally related to total air voids, specific surface area (S) and tortuosity (Al-Omari, 2004). It is a more complex relationship than the simplified Darcy equation as much more detailed information is required on the parameters mentioned before. The values or ranges of these properties can be determined through the use of X-Ray Cat Themography (CT) scan imaging and the accurate determination of voids content. The availability of equipment such as X-Ray CT scan is extremely limited. The development of the CoreLok equipment in recent years and improved accuracy versus standard laboratory methods to determine air voids makes this a very feasible method to measure air voids and porosity of asphalt mixtures. In Figure 2. 2 the CoreLok equipment is shown. XRay CT-Scan and to a lesser extent, CoreLok, are not yet freely available outside forensic and research investigations and is therefore not yet done as standard laboratory testing (Horak, et al., 2017b).
10
Figure 2. 2: CoreLok Equipment (Horak, et al., 2017a)
The CoreLok machine, can be used to indirectly determine permeability by measuring the porosity. This CoreLok method recognises that for fine graded mixes and the related design and quality control, it is adequate to measure percentage air voids, however, the percentage porosity is a more fundamental expression that can be related to permeability. Percent porosity gives an indication of the fraction of voids accessible to water. By this definition, it can be seen that percentage porosity is associated with the permeability of a sample. This method is excellent for connecting the concepts of porosity and permeability because two samples where the percentage porosity was determined with the CoreLok machine will have the same permeability. This is however proven to be untrue for two samples with the same percent air voids as determined in the traditional way. The CoreLok machine can thus determine a fundamental parameter such as porosity without using any additional assumptions. (Horak, et al., 2017a) In Figure 2. 3, the correlation between CoreLok air voids and those determined via the standard AASHTO T166 method is shown. It clearly shows the correlation relationship that indicates at increasing void contents the standard laboratory method may be under estimating air voids as determined by the more accurate CoreLok method. Using the AASHTO T166 method there are fundamental problems with estimating the bulk volume. If water can quickly permeate the sample through the interconnected voids, then logically when removing the sample from the water bath will cause the water to flow out of the sample with equal ease. Cooley (2003) states that the saturatedsurface dry (SSD) condition is often inaccurate, leading to underestimation of the bulk volume. (Cooley, 2003) 11
NCAT conducted tests to correlate the CoreLok obtained percent porosity to field and laboratory determined permeabilities (Harris, 2007). Some strong correlations were proven between these three test types. The strongest correlation, or relationship, (with R2 = 0.7137) was between the CoreLok determined porosity and the laboratory measured permeability. This less than perfect correlation was ascribed to the recognition that water is assumed to only flow in a vertical direction during laboratory testing. In field testing, the voids within the asphalt testing area can be interconnected in a horizontal direction and flow outside the water/surface contact area of the permeameter, which explains the lower correlation coefficients (R2 = 0.5436). (Cooley, et al., 2002; Cooley, 2003).
Figure 2. 3: Comparison of Air Voids Determined Using the CoreLok and standard tests (Cooley, 2003)
2.3.
Problems with Normal Permeability Measurements
Permeabilities in both laboratory and field situations are determined using Darcy’s law. Specifically, in field testing, the assumption of one-dimensional flow and still assuming water flow is true to Darcy’s Law, causes inaccuracies in the results using the falling head methodology. Generally, this causes frustration when comparing or correlating field and laboratory data for permeability. The conditions on site is far removed from the “ideal” conditions set up, or assumed, for laboratory testing. That being said, field permeameters still provides a good estimation of the permeability due to standard
12
testing methodology creating a basis for comparison between other field tests (Mallick R. , Cooley, Teto, & Bradbury, 2001). It was found that without taking core samples for visual inspections, that it is nearly impossible to know whether or not there may be boundary condition problems influencing the flow of water, or better described as the potential for water flow through a pavement layer (Mohammad, et al., 2003). In the field without core inspection of the horizontal permeability by looking at the specimen crosssection area, the directional flow is ensured in the laboratory with the constant head permeability test where the sides are sealed off. Mohammad, et al (2003) observed that during field testing using a falling head apparatus, the first drop in the water level generally took a much shorter span of time compared to subsequent testing at the same location. In field testing, there is an unsubstantiated assumption regarding the saturation level of the of the sample, or test location. Laboratory testing can easily circumvent the problem by controlling the saturation of the sample taken, whereas in field conditions there is little to no control pertaining to this, or way to measure it. The reasoning as to why this trend observed by Mohammad, et al (2003) occurs, is because it is thought that during this first falling head test, the voids are filled with water, including some that are typically “cul de sac-ed’ and additional suction force adds to the drawing in of water before full wetting occurs. During the second and third successive tests the water cannot travel through these unconnected voids and only flows through the interconnected voids and associated channels or pores and as a result, the water flow is less (Mohammad, et al., 2003). Cooley (2003) concluded there is a significant relationship between permeability and water absorption as measured by AASHTO T166 on cores cut from a roadway. The relationship appears to be more related to gradation shape than nominal maximum aggregate size. For coarse -graded mixes, a water absorption value of approximately 1 percent would define a point where a pavement becomes excessively permeable. It is also difficult to assess the boundary conditions at the bottom of the HMA layer of which the field permeability is being measured. When water is introduced into the standpipe of the falling head permeameter, it flows into the pavement. The water can flow in any direction and most likely flows outside the water/contact area of the standpipe (Cooley, 1999). Water can also flow through pavement layers, fine hairline cracks, or even the interlayer space that may exists (Gogula, et al., 2003). Other factors that may cause a false high permeability reading are; a tack coat that wasn’t applied evenly; if there was some deterioration of the tack coat; or improper installation that results in not achieving proper seal-off of the standpipe of the field falling head permeameter.
13
Harris (2007) developed a modified field permeameter for study of the water–pavement contact area (dependent on the permeameter diameter) and anisotropy effect on field permeability measurements. A reliable sealing system was created that was consistent, but not detrimental to the pavement surface. The results of the study showed that larger water–pavement contact areas yielded increasing influence on vertical flow, which better represented the one -dimensional flow conditions prescribed by the falling head method using Darcy’s law. Equations to calculate both vertical and horizontal permeability coefficients were developed with finite element simulations of the field tests as an axis-symmetric flow. Two permeability tests with two water/contact area sizes were conducted with this approach to obtain both vertical and horizontal permeability coefficients. The finite element simulations indicated that the nominal permeability calculation with one -dimensional assumptions was valid when the water–pavement contact area was large. This aspect is addressed in more detail in the section on directional permeability.
2.4.
Factors Affecting Permeability
The gradation shape and NMPS have similar effects on HMA permeability. Gradations that are considered fine-coarse mixes have lower permeability as compared to coarse graded mixes. The gradation shape affects the size of the individual void sizes within the pavement (Mallick, et al., 1999). Fine particles create small voids that are less likely to become interconnected voids for water to flow through. Coarse graded mixes create larger air voids that are more likely to be interconnected and create a more permeable pavement. Due to the fact that NMPS and gradation shape both affect permeability, by controlling the amount of fine aggregates in a mix, the permeability characteristics can also be controlled. By adding fine aggregates, the individual air void size can be decreased. This will create less possibility of interconnected air voids (Cooley, et al., 2001). In Figure 2.4 it is illustrated that Mallick et al (1999) found as the NMPS increases, the permeability also increases at a given void level. For instance, at an in-place air void content of 6 percent the following permeabilities were observed for each NMPS: 9.5 mm NMPS » 6 x 10-5 cm/sec 12.5 mm NMPS » 40 x 10-5 cm/sec 19.0 mm NMPS » 140 x 10-5 cm/sec 25.0 mm NMPS » 1200 x 10-5 cm/sec
14
Figure 2. 4: Effect of Nominal Maximum Aggregate Size on Field Permeability (Cooley, et al. 2002)
There is a reasoning that a larger proportion of coarse aggregates in the mix can possibly have larger voids. It must be added that by volume, a more fine-graded mix will likely have equal void percentage. Expanding on this concept, it can be said that coarse graded mixes with lower percentage fine aggregates are more likely to have interconnected voids. This can be further explained by binary aggregate packing principles, which indicate that a mix between coarse and fine material affects the combinations’ porosity in characteristic concave functions (Olard, 2011; Furnas, 1928). Such concave porosity functions for a binary aggregate combination are dependent on the size ratio (fine/coarse) as well as the coarse/fine volume ration or percentage of coarse aggregates in the mix. This is illustrated in Figure 2.5, known as the basis of a binary packing triangle graphs. At large coarse proportions in the mix (to the right of the dilation point on the X-axis in Figure 2.5) and lack of fine material in size and volumetric proportion, the combined porosity is high. This is explained by Olard (2011) as due to the wall and the loosening effect as illustrated and described in Figure 2.6. The combination of these effects in such aggregate fraction combinations, particularly coarse graded mixes, can influence the porosity significantly. With the wall effect the finer aggregate against the “wall” or surface of the coarse aggregate cannot be filled effectively leading to increased porosity and a high probability of interconnected voids. When the fine aggregate fraction also forces the coarse aggregate apart and preventing stone on stone contact additional voids are creased which increase the voids as well as the possibility of interconnectedness of the voids. Permeability is dependent on interconnectedness of percentage voids to permit flow of water.
15
Figure 2. 5: Typical binary aggregate combination porosity influence with varying diameter ratios and proportion of coarse aggregate (Olard, 2011; Furnas, 1928)
Note: 1. Coarse/large aggregate 2. Medium sized aggregate 3. Fine aggregate
Figure 2. 6: Schematic illustration of porosity influence due to the wall and the loosening effect (Knop & Peled, 2016)
Wall effect: When medium sized particles (2) are adjacent to the large or coarse aggregate (1) margins and consequently the packing density of medium aggregates are disturbed.The increased voids between the medium particles and the wall (coarse aggregate) is thus the wall effect. Loosening effect: When a fine particle (3) is in the matrix of medium (2) and coarse particles (1) and the small particle (3) is too large to fit into the interstices of the medium aggregate it disturbs the packing density of medium sized particles.
16
Experimental work on binary aggregate combination for filter beds as done by Mota et al. (2013), shown in Figure 2.7, shows that the coefficient of permeability (Y-axis of Figure 2.7) is affected by the coarse/fine aggregate volume ratios (X-axis of Figure 2.7). This is a case where the fine/coarse diameter ratio is 0.1. Therefore, the fines fit into the voids of the coarse aggregate without di fficulty, but still if the coarse aggregate content (volume) in the mix is increased it significantly influences the permeability. In Figure 2.7 the trend shown illustrates coarse/fine volume proportion ratios from 0 to 0.6 is virtually insensitive to the permeability measured. This is the zone where the fine aggregate fraction will be proportionally higher in the mixes as per Figure 2.5. For binary mixes with coarse/fine volume ratios above 0.6 (therefore more coarse material in the mix) the permeability coefficient increases significantly. Above the ratio of 0.8 the increasing trend is exponential. Hence, coarse/fine aggregate volume ratio of 0.6 can be seen as threshold value. This threshold value stays the same for other fine/coarse size ratios as well (Mota, et al., 2004). 10-2
K*106 m2
10-3
10-4
0.0
0.2
0.4
0.6
0.8
1.0
Volume ratio of large particles in the mix Figure 2. 7: Influence of coarse aggregate in the binary combination on permeability (Mota, et al., 2004)
Several researchers state that when the specific gravity of the material is the same for all the aggregate fractions, the volumetric ratio or proportion of coarse and fine aggregates can be taken as equivalent to the proportion of percentages retained on the coarse and the fine aggregate sieves as the basis of this simplified calculation (Butcher & van Loon, 2013). Therefore, it should be possible to benchmark the various binary aggregate combinations making up the macro, midi and micro levels so as to monitor where adjustments can be made to aggregate gradings to achieve packing efficiency as well as provide permeability control in a rational fashion. In Figure 2.8 the interconnectedness of voids is used by Cooley (2003) to classify HMA with NPS in the range of 12.5 mm and 9.5 mm as impermeable, permeable (variable) and permeable to highly permeable. The proposal is to use the intersection of tangents at approximately 4 % and approximately 10 % to determine the critical voids percentage where after the mix becomes 17
permeable. What is clear is that the zone in which the mix may become critical for permeability is defined as where the voids may be isolated and interconnected. It is hence variable and often mixes with voids around the critical void content may either be permeable or impermeable due to this variance in interconnectedness. (Cooley, 2003)
Figure 2. 8: Method for selecting critical air void and permeability values (Cooley, 2003)
The lift thickness is specified in a known ratio range of three to five to NMPS to ensure compaction can be achieved. The basis of NMPS implies that lift thickness of the asphalt layer will also influence permeability. A lower permeability is usually associated with an increased lift thickness (Mallick, et al., 1999). A possible explanation for this phenomenon it that thicker lifts holds internal heat for longer, facilitating the orientation of the aggregate particles during the compaction process. It is also well known that more effective compaction will have higher densities as a result, implying that there are less voids and that their probability for being interconnected is also low. These dense graded mixtures usually have smaller NMPS, like 9.5 mm. In summary, a thicker lift thickness can have higher density as a result, as well as lower potential for interconnected voids, and thus lower permeability. The inverse of this statement is also true in that thinner lift thicknesses can possibly have higher permeabilities (Harris, 2007) . Hainin, et al (2003) investigated these factors influencing permeability. The Bailey method determination of the defined coarse aggregate and the associated ratio (CA ratio) was done in the
18
sample preparation to better define the aggregate packing. The following regression equation was developed with all of the factors identified that influence permeability (Hainin, et al., 2003): Ln (k) = -19.2 + 5.96Ln(CL) + 1.47(CA Ratio) + 0.078(P12.5) + 0.0485(P1.18) + 0.00928(Ndes) – 0.0124(Ave. Thickness)
Where: Ln
= natural logarithm
k
= coefficient of permeability (cm/s)
CL
= air voids from CoreLok machine
CA Ratio
𝑯𝑺−%𝑷𝑪𝑺) = coarse aggregate ratio and 𝑪𝑨 = (%(%𝟏𝟎𝟎−%𝑯𝑺)
Where: Maximum Nominal Particles Size (NMPS) as per the Superpave definition: “One size larger than the first sieve that retains more than 10 % aggregate”; Half Size (HS) is defined as sieves size closest to or equal to half the NMPS; Primary Control Sieve (PCS) is the sieve size closest to 0.22X NMPS P12.5
= percent passing 12.5 mm sieve
P1.18
= percent passing 1.18 mm sieve
Ndes
= design number of gyrations of the gyratory compactor
It can easily be accepted that this correlation relationship is close to a universal relationship to determine permeability, as it contains the most obvious factors affecting permeability. The mix type, mix design procedure, etc., may however be different in different circumstances, which may lead to variability in the permeability values. The fact that density determined by gyratory compaction also limit this to laboratory prepared mixes only and therefore excludes field samples. The availability and general use of the CoreLok machine may also limit the possible use of this equation.
2.5.
Universal permeability relationship based on voids content
Traditionally voids percentage as related to density have formed the basis for permeability quantum definition as illustrated in Figure 2.8. Norambuena-Contreras, et al (2013) reviewed experimental studies regarding permeability coefficients based on laboratory and field conditions. They developed 19
a model to predict permeability of all types of asphalt mixes based on air void content. It was found that the spread in permeability coefficient (hydraulic conductivity) ranges are as shown in Table 2.1 with the description of the ranges and symbol suggested. Accordingly, the intended low permeability designed for dense graded asphalt to have an impervious or very low permeability (V lp) asphalt surfacings. This implies that the range of permeability should be below 1x10 -5 cm/s. Moderate to permeable layers would typically be in the range of 10-4 cm/s to 10-3 cm/s. (Norambuena-Contreras, et al., 2013) Table 2. 1: Permeability coefficient (hydraulic conductivity) (x 10 -5) (Norambuena-Contreras, et al., 2013)
Minimum
Maximum Description
Symbol
0.1
1
Very Low Permeability
V lp
1
10
Low Permeability
LP
10
100
Moderate Permeability
MP
100
1000
Permeable to Draining
P
1000
10000
Moderate to Free Draining
MFD
>10 000
Free Draining
FD
Figure 2.9 depicts an adaption of the practical guide introduced by Chen et al (2004). This guide divides a keen core visual observance into three separate categories, each with an associated air void distribution and connectivity. Horak et al (2011 a & b) illustrated this can be used during design and quality control as a calibrated, yet subjective measure of permeability potential as a first level classification based on keen visual observation of the cores. If the coring process disturbed the briquette face the core can also be cut in half to show two rectangular faces for inspectio n of the core inside. (Horak, et al., 2011a; Horak, et al., 2011b) Norambuena-Contreras, et al (2013) performed experiments with a wide spread of asphalt mixture types in an experimental design supplemented by available data in literature of measured hydraulic conductivity (permeability) of asphalt mixtures with different air void contents. In order to cover all asphalt mixture types and ranges of air voids associated with them, permeability tests on various types of asphalt mixtures where the air void content ranged from 0 % to 30 % were conducted. Therefore, the experimental design incorporated the range from impervious asphalt mixes (e.g. fine graded) to the highly permeable asphalt mixtures (e.g. porous asphalt). These tests were done in the laboratory with a Tri-axial cell for the impervious mixtures as well as a falling head permeameter for the more 20
permeable asphalt mixtures. Both apparatus types were found to give good reliable permeability measurements in the laboratory environment. (Norambuena-Contreras, et al., 2013)
10 -2
10 -4 to 10 -2
10 -4 or lower
Description of
Effective Good drainage
Semi-effective Poor drainage
Impervious
permeability
highly permeable
permeable
Porous asphalt
Stone Mastic Asphalt
Coefficient of permeability (cm/s)
condition Typical
Dense graded asphalt
asphalt mixture Figure 2. 9: Visual classification or interconnectedness of voids and permeability indication (adapted from Chen et al, 2004)
Norambuena-Contreras, et al (2013) used, as departure point, the Lucas-Washburn equation based on capillary flow in porous materials and modified it in their wide-ranging study of various asphalt mix types. This was verified by the wide range of asphalt mixtures tested (variance in air voids significant) which were supplemented by data available in literature which led to the development of the following universal relationship and as illustrated in Figure 2.10. -ln(k) = 45.97 (1/Avc) + 1.82 Where: ln
= the natural logarithm,
k
= the coefficient of permeability
Avc
= the air void content. 21
This equation can be used to predict the saturated permeability coefficient of asphalt mixtures with different porosity and different test methods. It must however be noted that in this case the NMPS varied between 16 mm and 11.2 mm. The larger NMPS effect described by Cooley et al (2000 & 2003) is therefore not fully included, but rather indirectly by the variance in void content. The basis of this relationship is contained in Figure 2.10. As can be seen, the relationship developed includes new asphalt from the laboratory situation as well as older asphalt from the road and other sources described in research literature used by them. (Norambuena-Contreras, et al., 2013) Figure 2.10 shows the asphalt mix types tend to have lower and higher values of k at the same air void content. This is a clear manifestation that air voids that are either inter-connected or isolated as described in Figure 2.9 tend to cause the variability in permeability, in spite of having the same air void content. It was also observed that older asphalt samples tend to deviate mostly to below the 50 % line. Their explanation is that older asphalt tends to have effects such as cracks, other damage, traffic loading induced rutting and accordingly changes to void orientation and linkage increase in voids. It is also affected by the possibility of pores being clogged by detritus and soil. They observed that the negative sign of the natural logarithm in the equation may be due to the fact that the theoretical model was developed under capillary flow through a saturated medium, while actually water infiltration of an asphalt surface in the field should have the opposite sign from the theoretical flow.
Figure 2. 10: Negative Logarithm of permeability of measured and literature values versus the inverse of air void content (Norambuena-Contreras, et al., 2013)
22
Ranges of average permeability conductivity coefficient values could therefore be assigned for various types of asphalt mixtures (dense; semi-dense; open graded; and porous asphalt) based on their air void content. These ranges are shown in Table 2.2. This can hence also be used to benchmark permeability coefficient determined in studies elsewhere. Table 2. 2: Statistical results for various asphalt mixtures (Norambuena-Contreras, et al., 2013)
Asphalt
Coefficient of permeability (cm/s)
Mixtures
Average
Std
Drainage description according
Minimum Maximum
to Table 2.1
deviation Dense
5.2 x 10 -6
0.134
1.9 x 10 -7
8.5 x 10 -5
Very Low Permeability (VLP)
Semi-dense
1.4 x 10 -5
0.312
2.2 x 10 -6
1.1 x 10 -4
Low Permeability (LP)
Open-graded
3.3 x 10 -3
0.201
1.2 x 10 -4
2.5 x 10 -2
Permeable to draining (P)
Porous
3 x 10 -2
0.271
2.6 x 10 -3
3.8 x 10 -1
Very permeable or moderate free draining (MFD )to free draining (Fd)
2.6.
Directional Permeability
Directional permeability is an important permeability subject that has been recognised and recently became the focus of investigations. In essence, the coefficient of permeability is determined in vertical and horizontal directions, made necessary by the anisotropic and heterogeneous nature of void distribution in HMA. The flow of fluid through the pore structure of HMA was modelled with aid of a finite difference method by Al-Omari (2004). In following years, Kutay, et al (2007) based their fluid flow model on the lattice Boltzmann technique (Harris, 2007). These studies used X-Ray CT imaging and the determination of porosity via the CoreLok apparatus. This was done to determine a threedimensional depiction of an asphalt core to analyse the flow of fluid through this core using a numerical simulation. Accurate data was obtained for the fluid flow characteristics and the distribution of air voids within HMA specimens. These studies modelled fluid flow in many lab and field specimens and concluded that horizontal permeability is much larger than vertical permeability. They found the horizontal/vertical permeability ratio was between 1 and 10. There were even specimens where this difference was larger than 30. (Al-Omari, 2004; Kutay, et al., 2007) Kutay, et al (2007) also developed a method to estimate directional permeability in various zones of the asphalt layer. A typical 100mm thick specimen was cut into three sections: upper (0-20 mm), middle (20-80 mm), and lower (80-100 mm). Kutay et al (2007) found percent porosity was higher in 23
the upper and lower sections of the samples. It was found that the horizontal permeability was well correlated with the average porosity in the upper and lower section and the vertical permeability was well correlated with the middle section. This implied denser compaction in the middle of the layer. This middle section consequently has lower horizontal permeability than at the bottom and top of the layer respectively. (Kutay, et al., 2007) Harris (2007) developed a laboratory permeameter which can measure horizontal permeability in asphalt layers. The core was drilled with either a 25 mm (1 inch) diameter or 63 mm (2.5 inch) hole in the centre, seal the top and bottom of the core and then feed the water in through a Perspex pipe which is a sealed off via two O-rings. He also varied the diameter of the falling head permeameter to determine what the influence is of the horizontal permeability on the vertical measurement of permeability. The experiments determined that an 88 mm (rounded up to 90 mm) (3.5 inch) diameter permeameter had the most influence on horizontal permeability. The 355 mm (14 inch) diameter permeameter was found to have the least influence of the horizontal permeability in an asphalt layer. These dimensions mentioned above also ensured that the permeability measurements of the horizontal and the vertical direction expose the same water to contact area to ensure the two directions are equal in terms of water/surface area exposure. (Harris, 2007) The influence of variable permeable interlayer conditions (e.g. tack coat condition) regarding the influence of underlying layers were also analysed via finite element method (FEM) models. Harris (2007) found that even though horizontal permeability cannot yet be measured accurately in the field that his laboratory experimental data confirmed the general tendency that horizontal permeability is larger than vertical permeability, but not necessarily by the same ratio ranges previously determined by Al-Omari et al (2004) and Kutay et al (2007). The results of one of the FEM analyses is shown in Figure 2.11. It clearly illustrates by means of flow lines that the smaller diameter has proportionally higher horizontal flow lines than the widest diameter where the proportional vertical flow lines are higher.
24
Figure 2. 11: FEM Flowlines /Pressure head (Kv/Kh= 5 and k=5x 10 -3 cm/s (Harris, 2007)
The data obtained from the field testing with four consecutive tests of the vertical flow falling head permeameter showed as the layer became more saturated, the permeability coefficient became lower. This confirms observations described earlier by Mohamed et al (2003). The largest change in permeability was generally between the first and second readings. This was when the pavement was almost dry and a significant amount of fluid has entered the pavement layer. There was generally a smaller change in permeability between the second to fourth tests. This would suggest that an acceptable level of saturation has to be met to give consistent results. A typical set of results with the varying diameter falling head permeameter is shown in Figure 2.12. It illustrates the observation made above as well as the difference due to diameter of the permeameter and the described influence of horizontal permeability (larger than vertical) on the smaller diameter and smaller contact area. Therefore, the saturation effect should be factored in by taking the third or fourth repetition values once saturation had been reached. (Mohammad, et al., 2003; Harris, 2007)
25
Figure 2. 12: Typical repetition and diameter of falling head permeameter effect (Harris, 2007)
2.7.
Conventional Aggregate Blending Methods
2.7.1. Maximum Density Line Asphalt mixtures have traditionally been designed using a trial and error procedures to determine aggregate gradation used in Hot Mix Asphalt (HMA) (Aurilio, et al., 2005). The distinction between coarse and fine aggregate has always been important because the main intent for coarse aggregate is to carry the brunt of the load in prevention or resistance to rut in particular. Coarse and fine aggregate are conventionally separated by the 4.75 mm sieve although, occasionally 2.36 mm is used when the Nominal Maximum Aggregate Size (NMAS) is close to 4.75 mm. Fine aggregate is considered as a material mainly used to reduce the voids developed in the coarse aggregate and to reduce the asphalt cement content (binder content in South Africa) to a desirable amount, without an excessive increase in coarse aggregate voids and without pushing the coarse aggregates apart which implies a loss of direct contact between them. The method that had traditionally been developed was to attempt to get the grading to adhere to the continuous maximum density concept as originally developed for concrete mixtures (Fuller & Thompson, 1907). The equation for Fuller’s maximum density curve is: P=100 (d/D)n Where: d is the diameter of the sieve in question P is the total percentage passing or finer than the sieve size, and D is the maximum size of the aggregate.
26
Fuller (1907) recommended a value of 0.5 for n based on his original work. Later research by Good and Lufsey (1965) applied the maximum density concept to asphalt mixtures and recommended n = 0.45 (Goode & Lufsey, 1965). This is illustrated in Figure 2.13 to follow.
Figure 2. 13: Typical Gradation Curve with the Continuous Maximum Density Line (Goode & Lufsey, 1965)
In practice, designing asphalt mixes have shown that deviation from the maximum density line is actually desirable as demonstrated in Figure 2.13. Gradations following the maximum density line may in fact be difficult to construct partly because there is not enough space for the bitumen binder to facilitate the lubrication needed during compaction. Lack of bitumen binder is also directly related to durability of the asphalt mixtures. Aggregates are normally blended by weight instead of volume. This might cause problems where the aggregate blend contains aggregates with large differences in density (e.g. a blend from different sources). Unless volumetric corrections are applied, it can result in excessive amounts of the lower density aggregate being incorporated into the asphalt concrete mixture. It is apparent that conventional gradation specifications provide little guidance for the selection of suitable gradations. A typical example is where steel slag with a known high specific gravity (SG) is blended with for example, dolerite fractions, with much lower SG. To help specify a proper aggregate gradation, The Strategic Highways Research Program (SHRP) initially suggested two additional features to the traditional 0.45 power chart to enhance aggregate gradation based on experience. They are: control points and a restricted zone. The control points
27
perform as ranges through which gradations must pass. They are illustrated in Figure 2.14. (Strategic Highway Research Program, 1994)
Figure 2. 14: SHRP specifications for Aggregate Gradations with NMAS 12.5mm (Strategic Highway Research Program, 1994)
The function of these control points and restricted zone are: •
To maximize the size of aggregate; to balance the relative proportion of coarse aggregate and fine aggregate and;
•
To control the amount of dust.
•
The restricted zone is placed along the maximum density gradation between intermediate size and the 0.3 mm size. It was introduced to avoid mixtures that have a high proportion of fine sand relative to the total sand portion. Typically, rounded sands will have a lubricating or “ball bearing type effect’ on mixes.
It also avoids gradations that follow the maximum density, which do not have adequate voids in the mineral aggregate. Several researchers showed that mixtures with aggregate gradations passing through the restricted zone have similar performance to other mixtures with aggregate gradations above or below the restricted zone. Therefore, this requirement was subsequently removed from the Superpave
28
specifications. Cooley et al (2002) also were some of the first researchers that found that coarser Superpave mix designs tend to show an increase in permeability measured after construction. (Cooley, et al., 2002) In NCHRP 673 (TRB (Transportation Research Board), 2011) it is suggested that the continuous maximum density (CMD) line can be used to check on proportioning of aggregate gradations. The CMD gradation is a useful concept based on the variation from maximum density gradation. This is calculated using an equation similar to that of the maximum density gradation as shown below.
Example: In a selected aggregate gradation the percent passing the 4.75 mm sieve is 84%.
Where; PCMD (d2) = percent passing, continuous maximum density gradation, for sieve size d2 d1 = one sieve size larger than d2 P(d1) = percent passing sieve d1 Percentage variation from the set CMD is thus PCMD (d2)-
The PCMD for the 2.36 mm sieve would be calculated as (2.36/4.75)0.45 × 84 = 73%. Therefore the variation from the maximum density gradation would be (84-73) = 11%
P(d1 )
The usefulness of the CMD gradation is that it allows a more realistic evaluation of how closely a given aggregate gradation follows a maximum density gradation compared to the traditional maximum density gradation. Because of its simplicity and flexibility, the CMD approach can be used along with other procedures. (TRB (Transportation Research Board), 2011) Figure 2.15 and Figure 2.16 shows how the CMD plot relates to changes in aggregate gradation for a series of 12.5 mm Nominal Maximum Particle Size (NMPS) aggregate blends: An SMA aggregate blend; a dense/coarse blend; a dense/dense blend; and a dense/fine blend. Figure 2.15 shows them with a traditional gradation plot, including the continuous maximum density gradation (CMD). Figure 2.16 shows the CMD plots for these same four aggregate blends and their % deviation per fraction size from the CMD. One of the features of the CMD plot is that it shows that despite the large differences in the four gradations, the fine aggregate portions of these blends are all reasonably close to a maximum density gradation. What it means is that the fine aggregate portions of all four blends, considered separately
29
from the coarse portion, follow a maximum density gradation relatively closely. This is deemed a very important concept when adjusting aggregate blends to meet Voids in the Mineral Aggregate (VMA) and/or air void requirements. Consider the dense/coarse gradation in Figure 2.15 and 2.16. In Figure 2.16 it appears that the fine aggregate portion of this aggregate deviates significantly from the maximum density gradation, and changing this portion of the blend might therefore tend to reduce VMA. However, it is clear from Figure 2.16 that attempting to reduce VMA by changing the fine aggregate portion of this gradation will probably be counter-productive, since it already closely follows a maximum density gradation. If a reduction in VMA is needed for this aggregate, the amount of material between the 2.36 mm and 9.5 mm sieves must be reduced. In general, the greater the deviation from the zero line on the CMD plot, the greater will be the VMA (and air void content) for the resulting mixture. In this way, the indirect influence on air voids and the possibility of interconnectedness of such voids, which is a prerequisite for permeability.
Figure 2. 15: Four different 12.5 mm NMAS gradations inclusive of CMD (TRB (Transportation Research Board), 2011)
30
Figure 2. 16: % Deviation from CMD gradation for the same blends as in Figure 2. 15 (TRB (Transportation Research Board), 2011)
In Figures 2.15 and 2.16, it appears that the largest differences in these blends are in the coarse aggregate. This is typical for aggregate blends used in HMA. However, the deviations from the maximum density gradation for the fine aggregate portion of many aggregate blends may seem small, but such differences can have a significant effect on the air void content and VMA of the resulting HMA mixture. The specific interpretation of CMD plots, such as those shown in Figures 2.15 and 2.16, is as follows: When using CMD plots to blend aggregates during a mix design, one should look not only at the amount of the deviation in different size ranges, but also the effect of these deviations on the air void content and VMA of the resulting mixture. The air void content and VMA for some mixtures might be most sensitive to changes in the coarse fraction of the aggregate blend, while other mixtures might be more sensitive to changes in the fine or intermediate portions of the aggregate blend. For this reason, HMA analyses tools should include, as part of the CMD plot, values for air void content and VMA for each aggregate blend (once they have been determined in laboratory testing). This makes it easy for the mix designer to determine what changes in the aggregate blends are most important in determining volumetric composition. Once the required change in VMA has been determined, the aggregate gradation for the existing mix design can be plotted, using a traditional gradation plot and the CMD plot. If an increase in VMA is needed, in general, the aggregate gradation must be modified to increase the difference between the CMD plot and the zero line. If a decrease in VMA is needed, the gradation should be modified to decrease this difference. This is shown in Figure 2.17. The heavy, dark line in thi s example is the 31
aggregate gradation for an existing mix design. It shows as the coarse aggregate portion of the gradation is increased, therefore moving away above the CMD, the likelihood that it will increase the VMA for this mixture is amplified. Change in coarse aggregate closer or below the CMD, shows it will probably decrease the VMA
Figure 2. 17: Effect of changes in aggregate gradation on VMA as shown on a CMD plot. (TRB (Transportation Research Board), 2011)
It should be noted that the zero line on the CMD plot is only an approximate indicator of the maximum density gradation - the actual position on the CMD plot of the maximum density gradation might vary somewhat from the zero line. Some mixes may show very high sensitivity to such suggested changes. This aspect is lifted out here to show that voids, via VMA control, is an indirect method to get “a handle” on permeability, but not any direct link to permeability is made here.
2.7.2. The Stone on Stone Contact Method A method for determining stone-on-stone contact was developed by Brown et al. (1997) for a specific asphalt mixture type; a special gap graded dense mixture called the Stone Matrix Asphalt (SMA) (Brown & Haddock, 1997). The proposed method first determines the voids in the coarse aggregate (VCAca) for the coarse aggregate only fraction of the SMA mixture. Secondly, the VCA all for the entire SMA mixture is determined. When the two VCA values are compared, the VCAall of the SMA mixture should be less than or equal to the VCA ca of the coarse aggregate only fraction to ensure that stoneon-stone contact exists in the mixture. This is in effect an indirect way of saying the porosity and therefore voids in the mix should be controlled or limited by the combination of the aggregates. Therefore, VCA all < VCAca, implies the fine aggregate is filling and reducing the voids between the coarse aggregate. Even though it is only applicable to SMA mixes, this approach doe s point the 32
direction towards the methods to follow, which aims to limit the porosity in the coarse aggregate potion and the combined mix with the fines included. It is clear that if a distinction can be made between the coarse aggregate in a mix and the fine portion, that the same basic proportioning principle should be valid for other mix types too and not just SMA type mixes.
2.7.3. The Power Law Method for Aggregate Evaluation Ruth et al (2002) suggested an approach to determine the slope and intercept (constant) of the coarse and fine aggregate portions of the conventional gradation curve using power law regression analyses as shown in Figure 2.18 (Ruth, et al., 2002). Note that in this case the distinction between the coarse and the fine portion of the mix is at the 2.36 mm sieve size. The format of the power law is:
PCA =aCA(d)n CA and PFA =aFA(d)n FA Where; PCA
= percent by weight passing a given sieve that has an opening of width d on the coarse
aggregate (CA) portion of the grading curve PFA
=percent by weight passing a given sieve that has an opening of width d on the fine
aggregate (FA) portion of the grading curve aCA
=intercept constant for the coarse aggregate
aFA
=intercept constant for the fine aggregate
d
=sieve opening width, mm
nCA
=slope (exponent) for the coarse aggregate (It can be for a coarse or a fine graded mix,
therefore n0.45) nFA
=slope (exponent) for the fine aggregates (The slope of the fine portion can also be
more or less than 0.45 as shown in Figure 2.18) Ruth et al (2002) conducted a study to correlate the gradation parameters with mixture performances. Gradation characteristics were identified that are detrimental to mixture properties. Specifically, gap graded or gradations with an excess amount of aggregate retained on a specific sieve did not yield properties equivalent to well balanced, continuously graded, aggregate blends. Greater bitumen binder content and percent passing the 4.75 mm sieve resulted in greater tensile strength and fracture energy (FE) for coarse graded mixtures. Lower FE was recorded for fine graded, long term aged, mixtures. The failure strain of fine graded mixture improved with increase in asphalt content and percent passing the 4.75 mm sieve. Another key finding was the surface areas (SA) of the aggregate 33
blends were found to be related to the aggregate characterization factors (percent aggregates passing the 2.36 mm sieve, nCA , nFA). Ruth suggests SA predictions based on gradation factors and effective asphalt content to estimate film thickness should be used.
Figure 2. 18: Power Law Gradation Evaluation (Ruth, et al., 2002)
This design method is therefore different in that the fines portion is analysed separately from a packing density point of view relative to the maximum density line. This not only has density of structural strength implications, but also size of voids and percentage, which impact on interconnectedness and accordingly, permeability. There is, however, no direct link expressed with permeability in this analysis approach.
2.8.
Bailey Method of Gradation Selection
2.8.1. Background Robert Bailey developed what is now known as the Bailey Method of gradation selection. The method places emphasis on stone on stone contact (strong aggregate skeleton) for a mix to be i) resistant to rutting and permanent deformation; ii) be durable; iii) have adequate voids in the mineral aggregate (VMA). A strong stone skeleton is essential because of the assumption that aggregates transfer, or resist, the applied repetitive vehicular load leading to deformation, which is normally measured as rutting on the pavement surface. Using a balanced gradation, the Bailey method facilitates aggregate interlock (stone on stone contact) as the major support of the structure to complete the mixture. 34
The Bailey Method’s main focus is to describe the packing of coarse and fine aggregates in terms of creating good aggregate interlock with stone on stone contact. The Bailey method is promoted in the SABITA Manual 35 (SABITA, 2016) (replacement of old TRH 8) with a whole appendix to explain it. (Zaniewski & Mason, 2006) In previous years, engineers tried to control the VMA by using the 0.45 power grading chart for aggregates. The process entailed modifying the gradation with regards to the maximum density line: a) farther away from the maximum density line to increase the VMA, b) or closer to the maximum density line to reduce the VMA. This was a procedure with a high risk of making a costly error seeing as there were no definite guidelines describing how changes to the grading would alter the mixture properties. Vavrik (2002) states that this method is based on properties associated with compaction, like air voids and VMA. In the true sense, the Bailey Method is a tool for engineers to design better HMA pavements and not a mix design method. This lends itself to the Bailey Method being used in conjunction with any other mix design method, such as (Zaniewski & Mason, 2006): •
SuperPave;
•
Marshall;
•
Hveem;
•
or stone matrix asphalts.
According to Aurilio et al. (2005), the Bailey method can be summarised by four key principles as demonstrated conceptually in Figure 2.19 (Aurilio, et al., 2005): 1. Create a distinction between coarse and fine fractions. Determine what creates voids and what fills the voids. Determine what size fraction controls the aggregate structure (i.e. the coarse aggregate or the fine ratio) 2. The way the coarse fraction is packed influences the way the fine fraction can be packed. 3. The fine aggregate coarse fraction relates to the packing of the overall fine fraction in the combined blend. 4. The fine aggregate fine fraction relates to the packing of the fine portion of the gradation blend.
35
Figure 2. 19: The four Bailey method principles (Zaniewski & Mason, 2006).
2.8.2. Basic Principles The Bailey Method is comprised of two fundamental principles to describe the relationship between mixture volumetric properties and aggregate gradation (Aurilio, et al., 2005). These two principles are: i)
aggregate packing and
ii)
defining coarse and fine aggregates.
The first of the two principles, aggregate packing, is defined as the orientation of aggregate (or stone) particles after compaction. How densely these aggregate particles are arranged will govern the percentage of voids in the mix. Regardless of the compactive effort or the compaction level achieved, the aggregates cannot occupy the total volume. The factors that influences the compaction level for asphalt mixes are: •
The type of compaction energy and the extent to which it was applied. The primary methods of compaction are static, impact, or shearing. The more compaction energy applied will result in higher densities, with respect to material type.
•
The shape and size of the aggregate particles. Rounder or rhombic particles can form a denser matrix than long, flat particles.
36
•
The surface texture and orientation of the aggregates. Smoother aggregates, as opposed to rough surface textures, have less friction resistance to overcome when moving into place within the aggregate matrix. Smoother textures further facilitate the movement of particles during the compaction process to orient themselves into denser packing patterns.
•
The grading of the mixture. Varied particles sizes can generally be arranged more densely than single-sized mixtures.
•
The strength of the particles and ability to withstand degradation. The type of particles and its material properties will influence the level of degradation that normally occurs during compaction.
•
Bitumen binder film thickness or effective film thickness. Binder content can be either too little or too much on both sides of the optimum binder content. If too little, obvious compaction difficulties may be experienced as well as durability problems in the long run. If too much, the mix may be tender during compaction and may also exhibit bleeding and deformation proneness.
The second principle is discerning between coarse and fine aggregates. The understanding of coarse aggregates are larger stone particles that have notable voids between the particles when placed in a comparative unit volume. The fine fraction of aggregates is smaller in size and can fill voids created by coarse aggregates. Conventionally, the defining sieve size that provided designation between fine and coarse aggregates was the 4.75 mm sieve. The Bailey Method however, makes use of the nominal maximum particle size (NMPS) of the mixture instead. The NMPS is the sieve size closest to the sieve where 10 % of the aggregate is retained, according to the SuperPave definition. The SABITA Manual 35 (SABITA, 2016) states that the NMPS for use in SA is where 15 % of the aggregate is retained. This adjustment was made to amend the more sand skeleton prone mixes in South Africa to reflect the same actual values as the Bailey method produces with the USA mixes. (Zaniewski & Mason, 2006) The primary control sieve (PCS) determines the distinction between what is seen as coarse or what is seen as fine aggregates, according to the Bailey Method (Zaniewski & Mason, 2006). It is important to note that the PCS referred to here is not the same as the standard SuperPave primary control sieve. Just as the NMPS differs for South Africa versus the USA, the PCS (based on the NMPS) can be different for every type of mix. The PCS can be calculated by the subsequent equation and must be rounded to the closest standard sieve size:
37
PCS = NMPS x 0.22 Where; PCS
= Primary Control Sieve for entire blend
NMPS = Nominal Maximum Aggregate Size for overall blend (one sieve size larger than first to retain more than 10%) These sieve sizes are indicated conceptually in Figure 2.20. The Coarse and Fine aggregate zones separated by the PCS sieve size have been colour super-imposed to show the two distinct aggregate size zones on the grading curve (Aurilio, et al., 2005).
Figure 2. 20: Separation between Bailey Coarse and Fine Aggregate for 19mm NMPS (Zaniewski & Mason, 2006).
The basis for the 0.22 factor multiplied with the sphere diameter, d (the equivalent of the NMAS or NMPS with aggregates) spacing is based on sphere packing theory with variance in the flat sides facing the space between the equal sized spheres. As can be seen in Figure 2.21, this spacing can vary between 0.15d to 0.29d. The average spacing for normally fractured (therefore flat faces) aggregates were determined to be 0.22d (Aurilio, et al., 2005).
38
Figure 2. 21: Estimation of Void Size between Aggregates for Bailey Control Sieves (Aurilio, et al., 2005).
2.8.3. The Volume-Density Ratios for Coarse Aggregate As stated earlier, the coarse portion of aggregates in the mixture creates voids. The volume of these voids must be calculated in order to determine the required volume of fine aggregate needed to fill the voids. For proper aggregate interlock, the mixtures are assessed based on volume but for simplicity the aggregates are checked on a weight basis. The coarse aggregates are subject to three governing weights: i) the loose unit weight (LUW), ii) rodded unit weight (RUW), and iii) the chosen unit weight (CUW). Figure 2.22 illustrates the loose unit weight and the rodded unit weight (Vavrik, 2002).
39
Figure 2. 22: Loose Versus Rodded Unit Weight Compacted Aggregate Samples (Vavrik, 2002)
The LUW is defined as the amount of aggregate filling a specified unit volume without any compactive effort applied. The LUW value represents the start value of aggregate interlock before compaction and is determined by the AASHTO T-19 Unit Weight and Voids in Aggregate procedure (Zaniewski & Mason, 2006). The procedure entails for the loose aggregate to be shovelled into a unit weight metal bucket and measured in the loose condition without any compaction. The LUW can then be calculated by dividing the mass of the aggregate in the metal bucket by the volume of the metal bucket, and expressed in kg/m3 (Aurilio, et al., 2005). The RUW is subsequently the amount of aggregate that fills a specified unit volume after specified compaction. The value for RUW represents the value of aggregate interlock after compaction. The RUW is also determined by the AASHTO T-19 Unit Weight and Voids in Aggregate procedure. The procedure entails filling the mould in three lifts, or layers, and rodding (compacting) each lift 25 times. AASHTO T-19 specifies increasing the container size to a function of the maximum aggregate size, yet, the recommended practice is to use a container with a volume of 0.25 ft 3 (0.0071 m3) for coarse aggregate and 0.0333 ft3 (0.00094 m3) for fine aggregates (Aurilio, et al., 2005). The Percent CUW is a value to the discretion of the designer and is decided based on the preferred interlock of coarse aggregate. Mixture designs can be a i) fine-graded mixture, ii) dense-graded mixture, or a iii) stone matrix mixture. After the mixture type has been decided on, the percent CUW can be nominated (Roberts, et al., 1996). The Percent CUW is subsequently determined by a percentage of the LUW of coarse aggregate. The typical range of Percent CUW for a dense graded coarse mixture is 95 % to 105 % of the LUW of coarse aggregate. This range will ensure some degree of coarse aggregate interlock. Values that exceed 105 40
% of the LUW of coarse aggregate generally have excessive aggregate degradation and are known to be difficult to compact in the field. When this Percent CUW is equal to or exceeds 110 %, they are considered SMA mixtures. SMA mixtures are notoriously difficult to construct, but are excellent at resisting rutting and providing better durability (Roberts, et al., 1996). The Percent CUW for a fine graded mixture is generally less than 90 % of the LUW of coarse aggregate. This implies that the load is supported largely by the fine aggregate structure. The range between 90 % and 95 % varies in and out of coarse aggregate interlock and should be avoided. Figure 2.23 shows the selection of the CUW based on the percent of LUW of coarse aggregate in the mix (Vavrik, 2002). The RUW of fine aggregates is necessary to determine the volume of fine aggregate required to fill the voids created by the coarse aggregate. This is essential in maximising the strength of the fine aggregate structure. The RUW of fine aggregate is once again also determined by the AASHTO T-19 Unit Weight and Voids in Aggregate procedure. The process encompasses rodding the loose fine aggregate by compacting it using a tamping rod. The RUW is determined, like LUW, by dividing the weight of the aggregate by the volume of the bucket in kg/m 3 (Vavrik, 2002).
Figure 2. 23: Selection of Chosen Unit Weight for Coarse Aggregate (Vavrik, 2002)
2.8.4. Control Sieve Sizes The combined blend is evaluated using three parameters: i) the coarse aggregate ratio (CA), ii) the coarse portion of fine aggregate ratio (FA c), and ii) the fine portion of the fine aggregate ratio (FAf). These parameters (See illustration in Figure 2.20) are calculated using the following designated sieves (Aurilio, et al., 2005): •
half sieve (HS): The half sieve is the closest sieve size to one half the nominal maximum particle size (NMPS), therefore HS= 0.5*NMPS closest sieve size.
41
•
primary control sieve (PCS): The primary control sieve is the closest sieve to 22 percent of the nominal maximum particle size (NMPS), PCS =0.22* NMPS.
•
secondary control sieve (SCS) : The secondary control sieve is the closest sieve to 22 percent of the primary control sieve size , SCS = 0.22*PCS
•
tertiary control sieve (TCS) : The tertiary control sieve is the closest sieve to 22 percent of the secondary control sieve: TCS= 0.22* SCS
As previously illustrated the ratio of 0.22 is based on variations of sphere packing theory. Table 2.3 details the sieve size for each designated sieve based on the NMPS for a Bailey coarse graded mixture (Aurilio, et al., 2005).
Table 2. 3: Typical factors for Bailey Coarse and SMA Graded Mixture Control Sieves (Aurilio, et al., 2005)
Designated sieves
NMPS (mm) 37.5
25
19
12.5
9.5
4.75
Half size
19
12.5
9.5
4.75
4.75
2.36
PCS
9.5
4.75
4.75
2.36
2.36
1.18
SCS
2.36
1.18
1.18
0.6
0.6
0.3
TCS
0.6
0.3
0.3
0.15
0.15
0.075
The pavement loads are carried by the skeleton of coarse aggregates for coarse- and SMA mixes. Bailey fine graded mixtures, however, has too much space between the coarse particles (they are not in contact with each other) to effectively transfer the load. This means that the load is carried by the coarse portion of the fine particles and consequently, voids in between the “large” fine particles must be filled by the “finer” fine particles. The sizes for the designated sieves for fine Bailey mixes are shown in Table 2.4. They will need to be redefined for the analysis. The half size now becomes basically half the original PCS. As stated before this type of stone skeleton is referred to as a sand skeleton in the South African asphalt mix design guide (SABITA, 2016). The coarse, or oversized, aggregate fraction therefore act as volume filler or “plum” in the mix (Aurilio, et al., 2005).
42
Table 2. 4: Bailey Fine Graded Mixture Control Sieves (Aurilio, et al., 2005)
Designated sieves
NMPS (mm) 37.5
25
19
12.5
9.5
4.75
Original PCS
9.5
4.75
4.75
2.36
2.36
1.18
Half size
4.75
2.36
2.36
1.18
1.18
0.6
New PCS
2.36
1.18
1.18
0.6
0.6
0.3
New SCS
0.6
0.3
0.3
0.15
0.15
0.075
New TCS
0.15
0.075
0.075
*
*
*
*Sieve too small to calculate-value not determined
2.8.5. Aggregate Ratios The coarse aggregate ratio, CA, gives clarification on the effect the coarse portion of aggregates has on the mixture. The equation to determine CA is shown below using the percent passing the half sieve (HS) and the primary control sieve (PCS). It is now well established that changing the CA (i.e. changing the volume of coarse aggregates) affects the aggregate packing, leading to changes in the VMA. It can then be said that the CA defines how the coarse aggregate packs together and subsequently, how the fine aggregate portion will fill the voids created. The calculation for CA is (Aurilio, et al., 2005):
Where; CA
= coarse aggregate ratio
PHS
= percent passing the half sieve (new half sieve (Table 2.4) for Bailey fine blend)
PPCS
= percent passing the Bailey primary control sieve (new PCS (Table 2.4) for Bailey fine
blend)
Logically, the coarse portion of fine aggregate ratio, FA c, will fill those voids created by the coarse portion of the aggregates. Zaniewski & Mason (2006) defines the FAc as the ratio that designates the
43
packing of the coarse portion of the fine aggregate and how creates voids that can be filled by the fine portion of the fine aggregate. The calculation for the FA c is as follows (Aurilio, et al., 2005):
Where; FAc
= ratio of coarse portion of fine aggregate
PSCS
= percent passing the secondary control sieve (new SCS (Table 2.4) for Bailey fine
blend) PPCS
= percent passing the Bailey primary control sieve (new PCS (Table 2.4) for Bailey fine
blend)
According to Zaniewski & Mason (2006), the fine portion of fine aggregate ratio, FAf, fills those voids created by the FAc. The FAf also defines the remaining voids in the entire fine aggregate blend from the comparatively smaller voids created. The equation to determine the FAf ratio is:
Where; FAf
= ratio of fine portion of fine aggregate
PTSC
= percent passing the Bailey tertiary control sieve (new TSC (Table 2.4) for Bailey fine
blend) PSCS
= percent passing the secondary control sieve (new SCS (Table 2.4) for Bailey fine
blend)
The equations discussed above for the three aggregate ratios are based on the values of percent passing each sieve size (Zaniewski & Mason, 2006). On Table 2.5 and 2.6 Zaniewski & Mason (2006) summarised the calculations for the aggregate ratios based on the nominal maximum aggregate size and type of blend. Percentage passing a specific sieve (e.g P 19 ) is used in these calculations.
44
Table 2. 5: Aggregate Ratios for Coarse and SMA Graded Mixtures (Zaniewski & Mason, 2006)
NMPS (mm)
Ratio CA
FAc
FAf
37.5
(P19 -P9.5 )/(100-P19)
P2.36/P9.5
25
(P12.5 -P4.75 )/(100-P12.5)
P1.18 /P4.75
P0.3/P1.18
19
(P9.5 -P4.75 )/(100-P9.5)
P1.18 /P4.75
P0.3/P1.18
12.5
(P4.75 -P2.36 )/(100-P4.75)
P0.6/P2.36
P0.15 /P0.6
9.5
(P4.75 -P2.36 )/(100-P4.75)
P0.6/P2.36
P0.15 /P0.6
4.75
(P2.36 -P1.18 )/(100-P2.36)
P0.3/P1.18
P0.075 /P0.3
P0.6/ P2.36
Table 2. 6: Aggregate Ratios for Fine Graded Mixtures (Zaniewski & Mason, 2006)
NMPS (mm)
Ratio
CA
FAc
FAf
37.5
(P4.75 -P2.36 )/(100-P4.75)
P0.6 /P2.36
P0.15/P0.6
25
(P2.36 -P1.18 )/(100-P2.36)
P0.3 /P1.18
P0.075 /P0.3
19
(P2.36 -P1.18 )/(100-P2.36)
P0.3 /P1.18
P0.075 /P0.3
12.5
(P1.18 -P0.6 )/(100-P1.18 )
P0.15 /P0.6
*
9.5
(P1.18 -P0.6 )/(100-P1.18 )
P0.15 /P0.6
*
4.75
(P0.6 -P0.3 )/(100-P0.6 )
P0.075 /P0.3
*
* Sieve size too small to be calculated – value not determined
The characteristics and behaviour of the asphalt mixture is wholly dependent on the three aggregate ratios. The CA ratio gives clarity the VMA and can predict possible construction problems such as, mixes prone to segregation of the coarse aggregate in the event that the CA ratio is low. Segregation is the clustering of coarse aggregates in one area creating a lack of coarse aggregate in another area. This is known to reduce the service life of an asphalt pavement. The FA ratios also denote changes in the VMA of the mix. Zaniewski & Mason (2006) recommended ranges for the three ratios. These are 45
shown in Tables 2.7 according to coarse and fine gradation type and Table 2.8 shows the recommended ratios for SMA mixtures (Vavrik, 2002). Table 2. 7: Recommended Ranges of Aggregate Ratios for Coarse and Fine Mixtures (Zaniewski & Mason, 2006)
Ratios
NMPS (mm) 37.5
25
19
12.5
9.5
4.75
CA (Bailey Coarse Blend)
0.80-0.95
0.700.85
0.60-0.75
0.50-0.65
0.40-0.55
0.300.45
CA (Bailey Fine Blend)
0.60-1.00
0.601.00
0.60-1.00
0.60-1.00
0.60-1.00
0.601.00
FAc
0.35-0.50
0.350.50
0.35-0.50
0.35-0.50
0.35-0.50
0.350.50
FAf
0.35-0.50
0.350.50
0.35-0.50
0.35-0.50
0.35-0.50
0.350.50
Table 2. 8: Recommended Ranges of Aggregate Ratios for SMA Mixtures (Zaniewski & Mason, 2006)
Ratios
NMPS (mm) 19
12.5
9.5
CA
0.35-0.50
0.25-0.40
0.15-0.30
FAc
0.60-0.85
0.60-0.85
0.60-0.85
FAf
0.65-0.90
0.60-0.85
0.60-0.85
2.8.6. New Bailey Ratios The particles smaller than the HS and larger than the PCS are termed ‘interceptors’ by Al-Mosawe et al (2015). Such interceptors can play an essential role in mixture’s volumetric properties. Al-Mosawe et al (2015) found by varying the proportions of interceptors that the VMA can be changed. This is in line with the principles demonstrated in an earlier section (see Figure 2.16) with regard to the variations from the CMD line. Interceptor particles are relatively large in comparison to the voids created by the larger particles and therefore cannot fill all those voids. It was discussed earlier regarding compaction that the shape, orientation, and elongation of the aggregate particles are all factors affecting the ability and probability of filling of the voids between coarse particles and interceptors. Typically, the suggested value of CA (1.0) ratio means that the weight amounts of 46
interceptors and coarser aggregate are equal and implied that there will be interlock between them. Through this the degree of compaction can logically be increased. A higher value for the CA ratio signifies disproportionate amounts of interceptor particles, which Al- Mosawe et al (2015) state may bring about segregation among particles larger than the PCS. This would potentially result in a difficultto non-compactible mix. The grading curve of mixes with high CA ratios generally have a noticeable S shaped (Al-Mosawe, et al., 2015). Al-Mosawe et al (2015) determined that the two standard Bailey ratios (SCS and TCS) are lacking when attempting to provide a broad understanding of aggregate structure and interlock. They developed two new ratios. The identified a gap in knowledge regarding the voids among the interceptor particles and the interlock between particles in the fine fraction with these interceptors. This is already an indication that there is recognition that the porosity aspect, which relates to implied density, as well as what was actually pursued by Al Masawe et al (2015), as they also refer to the Dominant Aggregate Size Range (DASR) method, but seemingly did not succeed in expressing it in terms of porosity. Both the concept of disruptor and the concept of contiguous aggregate ranges are in line with DASR methodology and description (Al-Mosawe, et al., 2015). Shang (2013) defines “pluggers” as the size between the Half Size (HS) and the NMPS. The plugger size description is also an important definition of the larger aggregate size, traditionally associated with the actual load carrying. It was observed that plugger size was not previously used or described as separate fraction, but rather mostly in combination with the interceptors as a combined unit making up that larger size stone skeleton or matrix. Al-Mosawe et al (2015) hence identified this gap in descriptors in the current Bailey method. They suggested this gap in knowledge could be filled by introducing two new ratios: i) mid-range coarse of fine over fine of coarse= C f / Fc and ii) all fines over the combination of pluggers and interceptor mid-range coarse aggregate= F/C. The Cf / Fc ratio is a depiction of the degree to which the coarse particles in fine fraction interact with and fill the voids in the interceptor fraction. The ratio is defined by the following equation (Al-Mosawe, et al., 2015);
The F/C ratio helps to describe the size range of aggregate which carries the brunt of the applied load by expressing a relative percentage of the fine and coarse materials. The F/C ratio is calculated as follows using the defined concepts from the Bailey method (Al-Mosawe, et al., 2015);
47
Al-Mosawe et al (2015) demonstrated that the ratios (CA, FA c, Cf / Fc and F/C) actually explains the packing of aggregate in the mixture better. They found that as Cf / Fc and F/C increases, the density and therefore also the stiffness of the mix tend to increase with good correlation. The reason for this is that the Cf / Fc ratio indicates to what extent the interceptor particle range is connected to each other and to what extent the voids between them are filled by smaller particles. They combined the effect of these four ratios in their relationship with stiffness as follows via correlation study (AlMosawe, et al., 2015); 𝐸=4412−245 𝐶𝐴+337 𝐹𝐴𝑐+573 𝐶𝑓𝐹𝑐⁄+1784 𝐹𝐶 Where; E
= Stiffness, expressed in P a
CA
= Coarse Aggregate Ratio
FAc
= Coarse Portion of Fine Aggregate Ratio
Cf
= Mid Range Coarse of Fine
Fc
= Fine of Coarse
F
= All fines
C
= the combination of pluggers and interceptor mid-range coarse aggregate
2.8.7. Effects on VMA in the Bailey ratios The four basic parameters of the traditional Bailey Method also communicate changes in VMA. The four parameters can be listed as: i) the percent chosen unit weight; ii) CA; iii) FAc; and iv) FAf. Bailey’s definition of the aggregate blend and whether it is considered a coarse- or a fine blend determines the effect that each parameter has on the VMA. Table 2.9 presents the common-understood effect on the VMA due to changes in the four parameters listed above (Vavrik, et. al., 2002). Table 2. 9: Effects of Increasing Bailey Parameters on VMA (Vavrik, 2002)
Parameter Percent Chosen Unit Weight CA FAc FAf
Coarse Blend increases
Fine Blend decreases
SMA increases
increases decreases decreases
increases decreases decreases
increases decreases decreases
48
Table 2.9 serves to show that increasing the Percent CUW causes an increase in VMA for defined Bailey coarse blends and conversely decreases in VMA for Bailey fine blends. Consequently, the minimum VMA predicted by the Bailey Method should logically be at the parting point where Bailey coarse and Bailey fine mixtures are separated. The separation point, according to Zaniewski & Mason (2006), is a CUW of 90 %. The connection between the change in CUW and the resulting influence it has on the VMA is depicted on Figure 2.24. The figure shows that as the mix changes from coarse to fine, the minimum predicted VMA value can be read off. The Percent CUW values less than or equal to 90 % denotes the fine mixtures. Similarly, the values 95 % to 105 % signifies coarse mixtures. SMA mixtures can be seen at values greater than or equal to 110 %. The region from 90 % to 95 %, as also expressed earlier in this thesis, represents the high probability of a mixture transferring in and out of coarse aggregate interlock and therefore must be avoided. The mixtures on both sides of this transition zone is likely to be more predictable with regards to performance when slightly varying the grading (Aurilio, et al., 2005).
Figure 2. 24: Chosen Unit Weight vs. Change in VMA (Aurilio, et al., 2005)
The defined coarse- or fine aggregate blend using the Bailey Method then logically also determines the degree of change in VMA. Table 2.10 details a 1 % change in VMA from changing Bailey parameters using Table 2.9 to gauge whether the change in VMA is either increased or decreased. Table 2. 10: Change in value of Bailey Parameters to produce 1% change in VMA
Percent Chosen Unit Weight CA FAc FAf
Change in Parameter Coarse Blend 4% change in Bailey PCS 0.20 0.05 0.05
Fine Blend 6% change in Bailey PCS 0.35 0.05 0.05 49
VMA is acknowledged to be a basic volumetric parameter which is indirectly related to porosity and void in the mix. Therefore, this is currently the only indirect control over the unknown concept of the interconnectedness of the voids and consequently, permeability. A more fundamental approach with more direct control over permeability is still needed, even from the Bailey ratios inclusive of the newer Al Mosawe (2015) ratios, which still focus exclusively on structural strength and not permeability per sé.
2.9.
Methods Using the Porosity as Criteria
2.9.1. Porosity of Granular and Soil Mediums Porosity (ƞ) is a dimensionless factor commonly used in the field of soil mechanics (Craig, 2004). It designates a relative ratio of voids to the total volume. This ratio is illustrated in Figure 2.25 for the various soil phases. Porosity, ƞ = Volume of voids/Total Volume =V v/V. Kim et al. (2006, 2009) showed that porosity can also be used for asphalt mixtures to establish a criterion which ensures contact between the dominant particles. They established that porosity of these dominant particles look like an excellent means for estimating potential rutting performance as well as fracture resistance of asphaltic mixtures (Kim, et al., 2006; Kim, et al., 2009).
Figure 2. 25: Relationship among soil phases (Craig, 2004)
50
In traditional soil mechanics (Freeze & Cherry, 1979; Lambe & Whitman, 1969), it has been determined that the porosity of granular materials should be no greater than 50 % ( ƞ < 0.5) for soil particles to have contact with each other, therefore to be interactive. The range of porosity is approximately 45 % to 50 % and is true irrespective of particle size and distribution. This gives a clue for asphalt mixtures and the porosity related to asphalt mix aggregates should therefore also be less than 50 %.
2.9.2. Porosity Principles Applied to Asphalt Mixtures 2.9.2.1.
Definition of Dominant Aggregate Size Range (DASR)
In order to convert the soil mechanics porosity principle to asphalt, the aggregate packing of the asphalt aggregate is first described since it relates to the formation of an aggregate skeleton or framework. Roque et al. (2006) and Kim et al. (2006 and 2009) hypothesized on this primary aggregate skeleton by stating that an interactive range of particle sizes exist that predominantly contributes to interlocking of aggregates. These larger stone particles consequently interacting with each other tend to form the primary structure of the asphalt mixture for contribution to resisting deformation. It is claimed that as such, it also contributes to resistance to fracture. This size range of aggregate, or stone particles, is called the Dominant Aggregate Size Range (DASR). DASR is defined as the interactive contiguous range of particle sizes that forms the dominant structural network of aggregate (primary stone skeleton). This is illustrated two dimensionally in Figure 2.26 for three basic aggregate grading types. (Roque, et al., 2006; Kim, et al., 2006; Kim, et al., 2009) Particles smaller than the DASR can be assumed to fill the voids between DASR particles, which is also referred to as the Interstitial Volume (IV). The Interstitial Components (IC) can therefore be listed as the materials including the asphalt binder, aggregates, and air voids that exist within the spaces of the DASR structure. The IC aggregates along with asphalt binder form a secondary structure in the mixture to aid deformation and fracture resistance. These components are also the chief source for adhesion and tensile resistance. In a sense, the IC accordingly provides stability for the DASR matrix. Larger particles than the DASR is likely to “float” in the DASR matrix, subsequently not playing a crucial role in the overall aggregate structure. This rings true with the typical sand skeleton continuously graded asphalt mixes where the large aggregate also “drift” in the sea of the sand skeleton size aggregate and do not contribute to structural strength of the asphalt mix.
51
Particles larger than DASR
Figure 2. 26: Schematic of DASR and IC concept for three basic grading types (Roque, et al., 2006)
2.9.2.2.
DASR Porosity
Kim et al. (2006) indicates that the concept of porosity can be utilised as a measure to guarantee contact between DASR particles in the asphalt mix. Therefore, facilitating satisfactory interlocking and good deformation and fracture resistance. The relationship between the various components in an asphalt mix is similar to that of a soil medium (see Figure 2.25). In Figure 2.27 the components of the asphalt mix is shown in diagram format. The Voids in Mineral Aggregate (VMA) of asphalt mixtures is a well -known concept used in asphalt technology. VMA specifies the volume of open space between aggregate particles in a compacted mixture, very similar to that of the volume of voids in soil. In the case of asphalt though, VMA also includes the bitumen binder volume. It is possible to calculate porosity for any DASR by making the assumption that mixtures must have a certain effective bitumen content and air void content (i.e. VMA) for specific gradings. There a number of basic principles required to calculate DASR porosity: i) The Voids in Mineral Aggregate (VMA) of asphalt mixtures indicating the volume of open space in between aggregate particles within a compacted mixture, which is analogous to the volume of voids in soil. ii) Porosity that can be determined for any DASR based on the assumption that an effective bitumen binder content and air voids (i.e. VMA) for any given aggregate grading exists for a mixture.
52
Vv (DASR)
VICAGG VT(DASR)
Figure 2. 27: Asphalt mixture components or phases (Chun, 2012).
DASR porosity for asphalt mixtures can therefore be calculated as follows:
ηDASR =
VV(DASR) VT(DASR)
or ηDASR =
VICAGG +VMA VTM − 𝐕AGG > DASR
Where;
η
DASR
= DASR porosity,
VV
= volume of voids within DASR,
V T(DASR)
= total volume available for DASR particles,
V ICAGG
= volume of IC aggregates,
VMA
= voids in mineral aggregate,
V TM
= total volume of mixture,
V AGG>DASR
= volume of particles bigger than DASR,
V
= volume of IC aggregates,
(DASR)
ICAGG
53
It is clear that this ideal composition of material has a DASR volume (V V(DASR) ) which is totally filled with the Interstitial Volume (IV), without disrupting the DASR interconnectivity or contact. The IC should fill the voids within the aggregates larger than the IC without disrupting the DASR structure. If the IV is however larger than the V V(DASR), disruption of the DASR structure takes place. This situation will be discussed after the basic porosity aspects of the ideal DASR model had been discussed. (Kim, et al., 2006; Kim, et al., 2009)
2.9.3. Effect of Grading Type on the Ideal DASR Model In Figure 2.28 a fine continuously graded mix (F1), a course continuously graded (C1) and an open graded mix represented by a Stone Mix Asphalt (SMA) are shown, versus the theoretical continuous maximum density (CMD) line. Roque et al. (2006) and Kim et al. (2006 and 2009) used this to illustrate that neither of these gradings actually meet the CMD. Of these gradings, the SMA is showing the most obvious deviation.
Figure 2. 28: Typical grading types versus the continuous maximum density (CMD) line (Roque, et al., 2006)
If the porosity of the individual fractions for these gradings are calculated the results are very enlightening. Such individual fraction calculated porosity values are shown in Figure 2.29, which indicate that it is only the 9.5 mm aggregate fraction of the SMA grading, which has an individual porosity below the threshold value of 50 % described before, that will ensure stone on stone or aggregate on aggregate contact. This is to be expected for the SMA grading as it is an open graded or 54
virtually a single sized grading. It is inherently designed to have stone on stone contact. It is also known that the fine as well as coarse continuously graded mixes (F1 and C1) does have good resistance to deformation as a complete grading. This is achieved in spite of the fact that their individual fractions have porosity values above 50 % and therefore on their own will not guarantee aggregate fraction interaction like the SMA. This is an indication that it must be a combination of contiguous aggregate gradings which forms the DASR and will also have porosity values below 50 %, ensuring the formation of the primary stone skeleton which resists deformation. (Roque, et al., 2006)
Figure 2. 29: Porosity values for individual fractions for three distinctive gradings (Roque, et al., 2006)
2.9.4. Variance in Ratio of Contiguous Fractions on Porosity Determining the DASR of dense graded mixes is not always straight forward like that of the SMA. There may be more than one contiguous grading that may qualify. In general standard sieve sizes tend to have a 2:1 ratio. Roque et al (2006) and Kim et al ( 2006 & 2009) therefore used packing theory based on a hexagonal packing of large and a smaller spheres in this 2:1 combination to develop criteria in order to determine at what combination of the large and small particles (contiguous grading pair) the space between them are minimized (therefore lower combined porosity than 50 % as main aim). In Figure 2.30 the spacing between large and the smaller contiguous fractions are shown as the combination or ratio changes from 100 % large aggregate (similar to the SMA), where the spacing between particles are the least. As more small particles are added the spacing between the large 55
particles gradually increase, while the spacing initially between the smaller particles are large, showing that as their proportion increases the space between them decreases. From the interaction of the two particle sizes it can be seen that once 80/20 (large/small) ratio is reached the spacing for both the large and small particles reaches a combined optimum or minimum. If the ratios of large/small further decrease (meaning lesser large and more small particles), the large particles clearly move further apart while the space between the smaller particles clearly decreases.
Figure 2. 30: Spacing change between particles in a hexagonal configuration for a 9.5 mm and 4.75 mm aggregate contiguous combination (Roque, et al., 2006)
In Figure 2.31 the change in spacing (slope of change) is shown for the proportions of the large and small particles. Roque et al (2006) and Kim et al (2006 & 2009) determined that a maximum of 70 % of the large particle, as well as for the smaller particle, is a practical threshold value where after the slope of change or change in distance between particles of both particles tend to increase exponentially. Hence, the ratio should be limited to be below 70/30 (large/ small or even the reverse) in order to use the slower rate of change (slope values) for both large and small particles to determine a contiguous grading combination with a combined optimized spacing limitation and as follows, implied lower porosity. Below this 70/30 ratio the spacing change and spacing itself, is therefore stable. In other words, the slope of change is gradual. It is also observed that once the 70/30 ratio is exceeded, one particle will actually disrupt the adjacent particle to effectively interact and push them 56
aside. By doing so it effectively also negates the structural integrity of the combined skeleton to resist deformation. Typically, once the 70/30 ratio is exceeded, the smaller particles start separating the larger particles and force them to float in the smaller particle matrix and therefore their contribution to structural strength of the matrix is negated. All contiguous particles decided to be interactive are seen as part of the DASR and act as a unit to determine or regulate the porosity.
Practical threshold value Ratio zone where combined change in spacing of binary combination is minimized
Figure 2. 31: Change in spacing (Slope) for a binary (contiguous) mixture
2.9.5. Interaction Diagrams and DASR Porosity In Figure 2.32 the ratio between successive contiguous fractions for the three different gradings (originally shown in Figure 2.28) are shown. This is also known as an interaction diagram. The limits of 70/30 are indicated and the zone for good ratios of large/small contiguous fractions that can qualify as the DASR are highlighted in green. This zone would be where the combined spacing distance, and therefore combined porosity, would be optimal or smallest and below the 50 % threshold value set before. The traditional separation between large and fine aggregates are 2.36 mm and happens to coincide with the separation between DASR and IC: passing the 2.36 mm sieve, but retained on the 1.18mm
57
sieve. Therefore, contiguous fraction combinations left of the 2.36 - 1.18 mm line will have potential to be the DASR with the lowest porosity. Both the SMA and the coarse continuous (C1) gradings show contiguous combinations 4.75/2.36 mm and 2.36/1.18 mm, which show good interaction. The fine continuous (F1) grading shows three pairs of contiguous fractions (9.5/4.75, 4.75/2.36, and 2.36/1.18) that may have good interactions. Therefore, there are a number of contiguous combinations that have good particle interaction and that needs to be checked whether it gives the lowest porosity.
Figure 2. 32: Interaction diagram for three types of grading (Roque, et al., 2006)
In determining DASR for these three gradings, the lowest porosity of any of the contiguous fractions were determined by first defining the largest fraction’s porosity. As shown in Figure 2.33 it is only the SMA grading with the single size of 9.5 mm that actually has a porosity below 50 % ( ƞ = 42 %). The contiguous fractions of the Fine (F1) and Coarse (C1) mix gradings all had the porosity values of the individual coarse fractions of the contender for DASR above 50 %. However, when the porosity of these contiguous fractions were determined by Roque et al (2006) and Kim et al (2006 & 2009), they all came in below 50 %. The coarse continuous grading even showed lower porosity (for C1, ƞ =36 %) than the SMA and the fine continuous grading (for F1, ƞ =46 %).
58
Figure 2. 33: Porosity of individual fractions and combined as contiguous combination
2.9.6. The DASR - IC Model Characteristics 2.9.6.1.
Interstitial Component (IC) of Mixture Gradation
The total mix component constitutes the DASR and the IC, therefore the DASR-IC model. Figures 2.25 and 2.27 show that the interstitial component (IC) is the material including bitumen binder, fine aggregates, and air voids that exists within the interstices of the DASR. The volume of this material is considered as the interstitial volume (IV). Guarin (2009) showed that properties and characteristics of the IC will strongly influence the rutting and cracking resistance of asphalt mixtures. (Guarin, 2009) As previously alluded to, the IC should fill the voids within the aggregates larger than the IC without disrupting the DASR structure. In that proportion it will give stability to the total matrix within the mix and particular, the relative stability of the DASR - the main element which carriers the load and resists deformation and rutting of the asphalt mix. If the IC or IV overflows or become bigger than the actual DASR volume, the disruption of the interconnectedness of the DASR particles occur. This disruption effect is described in the following section and characterised in terms of its influence on rut and crack resistance of the total asphalt mixture. (Guarin, 2009) 2.9.6.2.
Disruption Factor (DF)
Guarin (2009) developed this term and concept to determine the potential of the finer portion of the mixture’s gradation to disrupt the DASR structure. DF can effectively evaluate the potential of IC particles to disrupt the DASR structure. DF can be calculated using the following equation: 59
It is evident that there would be three ranges of DF values with associated possible behaviour of the asphalt mix. This is conceptually illustrated in Figure 2.34 (Guarin, 2009; Chun, 2012). The three DF ranges are also briefly described as follows, purely in terms of the aggregate interaction, therefore initially ignoring the influence of the bitumen binder and effective film thickness. •
DF < 1 implies the DASR is fully interconnected and hence fully capable of load bearing and aiding rut resistance. No additional support or benefit from the IC particles to the DASR loadbearing is possible.
•
DF = 1 implies it is an optimum mix proportion between DASR and IC where the DASR is the primary load bearing skeleton. Stability of the total matrix may be a concern if other factors such as particle form, morphology and binder content start to influence the DASR matrix stability.
•
DF > 1 implies the DASR particle interconnectedness is pushed aside, or disrupted by the excessive IC volume. Therefore, the DASR structure cannot be the primary load bearing internal structure or skeleton. The load bearing function is consequently transferred to the IC.
DF1
Figure 2. 34: Illustration of Disruption Factor ranges versus DASR composition (Gaurin, 2009).
2.9.6.3.
Ratio between Coarse Portion and Fine Portion of Fine Aggregates (CFA/FFA)
The previous section indicated that the IC often has to take on greater contribution to the overall load carrying capability of the total asphalt mixture. Therefore , the characteristics of the IC particle fractions must be better described. The effect of particle interaction within the IC is described by a new parameter: CFA/FFA. Figure 2.35 describes or demonstrates the basic principle of determining the CFA/FFA. CFA is the IC portion on the gradation curve between the 1.18 mm sieve and the 2.36 60
mm sieve. FFA is the portion of the IC between the 1.18 mm sieve and inclusive of the 75 micron sieve. (Chun, 2012) The CFA/FFA is hence the ratio between the coarse portion and fine portion of the IC particles. The CFA/FFA was introduced to characterize the structure of the IC of mixture’s gradation (Chun, 2012). The CFA/FFA can be used as an indicator to represent the fineness and aggregate structure of the IC. CFA/FFA is related to the creep response or time-dependent response of asphalt mixture. (Guarin, 2009) The insert in Figure 2.35 shows how the grading curve variance in the IC zone can cause variance in the quantum of the CFA/FFA ratio and how the creep rate (related to rut development) can be negatively influenced by either too low or too high values of CFA/FFA. The aspect of the bitumen binder is excluded for discussion purposes, only because it is known that the specific area of the finer portion of the IC has traditionally been associated with binder film thickness determination and is bound to have an impact on the CFA/FFA ratio additionally. (Chun, 2012)
CFA FFA
0.075mm
1.18mm
2.36mm
4.75mm
Figure 2. 35: Determination of the CFA/FFA of the IC (Chun, 2012)
61
2.10. Linking Bailey Ratios with Porosity and Permeability Control 2.10.1. Introduction Horak et al (2017a) analysed both the Bailey method and the DASR method and confirmed the principles of both approaches are based on volumetric packing optimization. As shown in the description of these methods. They tend to follow different approaches with different concepts associated with describing the desired optimized aggregate packing. The Bailey method typically identifies large and fine aggregate fraction ranges based on an established volumetric ratio. The method uses loose and rodded coarse aggregate compaction to establish the mix design type and to establish what voids are to be filled by the finer aggregate range to establish an optimum grading using trial and error methods. Horak et al (2017a) states the DASR method makes use of porosity, as a fundamental property, to evaluate the packing efficiency. In this regard, the DASR method is viewed as theoretically more fundamental or correct in explaining the concerns with regards to packing of the aggregate grading. Porosity relates not only the packing efficiency, but also the voids in the mix, density, and therefore states it is more directly related to permeability of the mix. (Horak, et al., 2017a)
Horak et al (2017a) points out that the Bailey and DASR methods have not been properly correlated or articulated to date. These two methods are often perceived as giving conflicting or confusing descriptions of the same aspects of the HMA/WMA. Horak et al (2017a) report as example that Dennemann et al (2007) used guidelines set by Khosla & Sadasivam (2006) to use both the Bailey and the DASR method in the analysis of the performance and indirect indication of permeability, but was not successful to link either of their parameters or ratios clearly to permeability. In the process, Denneman et al (2007) simplified the original DASR porosity formula to enable easy calculation of porosity of a single fraction. The original formula and the Denneman et al (2007) simplification is summarised by Horak et al (2017 (a & b) in Table 2.11. (Horak, et al., 2017a), (Horak, et al., 2017b), (Denneman, et al., 2007; Khosla & Sadasivam, 2006)
62
Table 2. 11: DASR porosity formulas (Horak, et al., 2017a)
Equation 1
η𝐷𝐴𝑆𝑅 =
𝑉𝑉 (𝐷𝐴𝑆𝑅) 𝑉𝑇(𝐷𝐴𝑆𝑅)
(Kim et al., 2006)
=
Equation 2
𝑉𝐼𝐶𝐴𝐺𝐺 + VMA
η(4.75−2.36) =
𝑉𝑇𝑀 − 𝑉𝐴𝐺𝐺 > DASR
Where:
(Denneman et al., 2007)
PP2.36 )( 𝑉𝑇𝑀 −VMA ) + 𝑉𝑀𝐴] 100 PP4.75 [( )( 𝑉𝑇𝑀 −VMA ) + 𝑉𝑀𝐴] 100
[(
Where:
η𝐷𝐴𝑆𝑅 = 𝐷𝐴𝑆𝑅𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦
ƞ(4.75-2.36) = Porosity of a typical fraction
V Interstitial volume = Volume of IC aggregates plus VMA,
hence
inclusive
passing 4.75mm sieve and retained on 2.36mm sieve
of
bitumen binder volume;
PP2.36 = %age particles passing 2.36 mm sieve
V AGG>DASR = Volume of particles bigger than DASR; V TM = Total volume of mix; V T(DASR) = total volume available for DASR
PP4.75 = %age particles passing 4.75 mm sieve VMA = Voids in mineral aggregate V TM = Total volume of mix
particles; V V(DASR) = volume of voids within DASR; V ICAGG = volume of IC aggregates; VMA = voids in mineral aggregate; V ICAGG = volume of IC aggregates.
Horak et al (2017a) did a critical review on the DASR and the Bailey methods and described rational Bailey ratios which adhere to the DASR principles of porosity and contiguous aggregate fractions on the grading envelope. Horak et al (2017a & c) then managed to link the rational Bailey ratios to the concepts of porosity and permeability previously described in the section on permeability. Lastly, Horak et al (2017c) shows how the aggregate grading can be broken up into macro, midi and micro level skeleton infill structures which enabled the link with the Binary Aggregate Packing (BAP) model, which has a more direct link with porosity and permeability. This development is described in the sections to follow. (Horak, et al., 2017c)
63
2.10.2. Bailey Ratios Revisited The control sieves associated with the Bailey method are indicated in the grading curve of a typical 19 mm continuously graded asphalt mix in Figure 2.36 (Shang, 2013). These control sieves are important as it defines aggregate fraction ranges which are used in the calculation of the Bailey ratios. As shown in Figure 2.36, Horak et al (2017a) subdivided the Bailey coarse aggregate range into a midi level and macro level based on the direction given by the work by Al-Mosawe et al (2015). As discussed before, Al-Mosawe et al (2015) indicated that the midi-section of the grading envelope is actually the most significant in terms of structural strength contribution. For that reason the midi -range is additionally also sub-divided into the plugger and interceptor ranges. Their significance in was highlighted by the new Bailey ratios defined by Al-Mosawe et al (2015). In Table 2.12 the original Bailey ratios are summarized by Horak et al (2017a) with a brief description in terms of aggregate ranges.
Micro Level
Midi Level
Macro Level
plugger
Figure 2. 36: Description of Bailey control sieves and aggregate ranges (Shang, 2013)
64
Table 2. 12: Existing Bailey ratios description (Horak, et al., 2017a)
Matrix
Gradation
Ratios
Summary explanation of Bailey ratios and parameters
Level The interceptors are the finer of the coarse aggregate Coarse aggregate ratio (CA) *
range between HS and PCS. Interceptors tend to ‘plug’ and reduce the larger aggregate framework or skeleton. Coarse aggregate between half size (HS) and primary
Macro
control sieve (NMPS) are called interceptors. 𝐂𝐀 =
(% 𝐇𝐒 − %𝐏𝐂𝐒) (% 𝟏𝟎𝟎 − %𝐇𝐒)
Large aggregates tend to have large porosity values and the same is true for the Interceptors and Pluggers on their own. However, when combined they show a significant reduction in the combined range of aggregate fraction
% 𝐈𝐧𝐭𝐞𝐫𝐜𝐞𝐩𝐭𝐨𝐫𝐬 = % 𝐀𝐥𝐥 𝐩𝐥𝐮𝐠𝐠𝐞𝐫𝐬 + 𝐥𝐚𝐫𝐠𝐞𝐫 𝐚𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞
porosity. Therefore, as these fractions do not overlap in size and the Interceptors tend to fill voids of the large aggregate skeleton, they should have a significant impact on the void size reduction and porosity. This ratio provides an indication of the stability of the
Midi
Fine aggregate coarse ratio
sandy fraction range in support of the larger aggregate packing (macro structure). Original value of 0.35 was
(FAc)
recommended for structural stability. Range is however typically 0.25 to 0.5 in practice. It is, therefore, expected 𝑭𝑨𝒄 =
%𝐒𝐂𝐒 %𝐏𝐂𝐒
that this ratio may be insensitive to permeability control.
Original FA f value of 0.35 was recommended for structural Fine aggregate fine ratio (FAf)
stability. Range is, however, typically 0.25 to 0.5 in
Micro
practice. This ratio’s fractions, though, fill the voids in the midi structure and, therefore, will have a further reduction 𝑭𝑨𝒇 =
(%𝐓𝐂𝐒) ( %𝐒𝐂𝐒)
potential on the size of the voids, but will not necessarily have a clear influence on the interconnectedness of these voids.
*Note: Horak et al (2017a) switched Plugger and Interceptor, corrected here
65
2.10.3. Permeability Control via Bailey Ratios From research done attempting to link these Bailey ratios to permeability, it was found that only the coarse aggregate ratio (CA) showed any correlation. As mentioned before Horak et al (2017a & b) described how Denneman et al (2007) used the original Bailey method ratios shown in Table 2.12 and the DASR principles to see to what extent it could adhere to the permeability control guidelines provided by Khosla & Sadasivam (2006). In Table 2.13 the original permeability control criteria set by Khosla & Sadasivam (2006) and those found by Denneman et al (2007) are integrated on the same parameters. As mentioned above, only the CA ratio (an original Bailey ratio) showed any sensitivity to permeability control. What became clear to Horak et al (2017a) is that the other two Bailey ratios did not show any sensitivity and control of permeability. Therefore, such permeability control tended to come from individual or combined aggregate fractions in the midi-range of the aggregate grading. It seemed that the fine portion of the grading in either ratios or single fractions were not fully recognised due to suspected problems with the FAc and FAf ratios to be highlighted later (Horak et al, 2017a). Khosla & Sadasivam (2006) did not refer to the DASR porosity at all and it seems that Denneman did link the porosity of the full DASR aggregate range and the Khosla & Sadasivam (2006) identified individual aggregate fractions, or contiguous aggregate fractions, to determine their possible control on permeability. Horak et al (2017a) therefore added the column describing the aggregate range s in terms of DASR terminology in Table 2.13. A critical review of these results showed Horak et al (2017a) that the ranges of control determined by Denneman et al (2007) and Khosla & Sadasivam (2006) seems to be very specific to the type of grading used. This implies that it cannot be used universally on all gradations. (Horak, et al., 2017a)
66
Table 2. 13: Suggested permeability control ranges and ratios (Horak, et al., 2017a)
Permeability indicator
Range
for
low Range for high DASR fraction range
permeability
permeability
CA
>0.5 (D)
0.5
Large Aggregate
>0.65
P/O
>6
Pluggers
0.65
Interceptors + pluggers
>0.52
Interceptors and
>0.7
Macro
Midi
Cf/FC
0.5 (D)
0.62
0.79
60% to 65% (67% D)
59
63
23% to 30%
13
15
% passing 2.36 mm sieve
35% (32%D) to 45% (47%D)
34
35
% passing 1.18 mm sieve
20% to 25%(10%
10.5
11
>35% (D)
58
59
1.5 to 2 (D)
1.4
1.52
CA % passing 4.75 mm sieve % retained on 4.75 mm sieve
% retained on 1.18 mm sieve % passing 1.18 mm sieve and % passing 2.36 mm sieve Ration of 2.36 mm sieve /1.18 mm sieve
(D) adjustments and ranges suggested by Denneman et al. (2007)
119
Both data sets (US2 and US3) show some parameters indicating potential for permeability, but not conclusive that these sections may have permeability problems. At this macro view, or first order data analysis, it even appears as if US3 meets more criteria for low permeability than US2. This is contrary to what is observed on the road sections, as US2 is sound while US3 shows permeability problems via seepage and pumping. The critique previously levelled by Horak e t al (2017a) was that these factor ranges are either specific aggregate grading bound, or too insensitive to be used as an accurate predictor of permeability. The grading factors listed in Table 4.1 only indirectly represents the basic permeability building block of actual porosity in the aggregate gradings. It was hence, concluded that they are relatively insensitive to actual permeability measurements, but can act as a combined indirect indicator of probability to permeability propensity. From this first order analysis it can consequently be stated that both US2 and US3 might have minor problems with a propensity to be permeable. Horak et al (2017a & b) reworked available data and developed criteria for the rational Bailey ratios and their porosity calculations based on these associated contiguous aggregate fraction ranges. The average values of rational Bailey ratios and associated ranges for low permeability are shown in Table 4.2 (Horak et al, 2017a&b). As in the case of Table 4.1, the benchmark criteria of green and amber were also applied to Table 4.2. Both the US2 and US3 data sets show problems with the larger aggregate ratios such as P/O, but these are not significant as Horak et al (20017a&b) found that the actual linked, or interconnected voids, are more closely linked to the finer portions of the aggregate grading, such as FA cm and FAmf. Such interconnected voids ultimately determines porosity potential. Therefore, it is significant that for both US2 and US3 data sets the FA cm and FA mf average values indicate probable permeability. Table 2.17, discussed earlier, summarises the parameters that showed clear sensitivity to permeability control . Table 4.2: Suggested permeability control criteria for Bailey ratios ranges (Horak et al, 2017a&b)
Bailey Ratios
Suggested range
US2 Data set
US3 Data set
CA
>0.5
0.62
0.79
P/O
>6
2.31
2.5
I/P
>0.65
0.9
1.1
Cf/FC
0.7
0.44
0.44
Plugger + Interceptors + all Fines
F/C
>0.65
0.23
0.23
Coarse of fines
FAcm
