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KSCE Journal of Civil Engineering (2013) 17(7):1630-1636 Copyright ⓒ2013 Korean Society of Civil Engineers DOI 10.1007/s12205-013-1279-8

Highway Engineering

pISSN 1226-7988, eISSN 1976-3808 www.springer.com/12205

Simple Methods for Approximate Estimation of Rehabilitation of Low Volume Roads Dae-Wook Park* and Jose Leo Mission** Received June 3, 2012/Accepted December 27, 2012

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Abstract This study presents two methods for the prediction and estimation of Maintenance and Rehabilitation (M&R) works based on the Highway Present Condition Index (HPCI) and statistical trend of rehabilitation period that is applied to paved low-volume roads. Since low volume roads comprised the majority of the country’s highway pavements having damage and distress characteristics that are totally different from high volume roads, specific criteria and guidelines are established. The results of the study have shown that economical costs for M&R must be viewed cumulatively in a specific M&R time frame compared to a yearly evaluation of individual costs. In particular, it has been shown that lower criteria of HPCI for rehabilitation within the serviceable level, although predicting higher individual costs at the later times, are still found to be the most economical option compared to service level criteria with higher HPCI values. Similar trend of results are found with a criteria for M&R based on maximum periods of rehabilitation, which showed a more economical choice compared to that when average and frequent periods of rehabilitations are made. Keywords: low-volume road, maintenance and rehabilitation, highway present condition index, rehabilitation history, cost estimation ··································································································································································································································

1. Introduction In the past the development of decision-support tools for identifying and prioritizing pavement Maintenance and Rehabilitation (M&R) needs has focused primarily on high-volume facilities. There is then a lack of distinct guidance and policies on maintaining, rehabilitating, and determining appropriate surface types for Low-Volume Roads (LVR). Since the vast majority of pavements worldwide can be classified as low-volume, they should be evaluated separately in the networks in which the M&R practices on these roads are vital to their continued serviceability. Several researches have been conducted on low volume pavements or roads. Kim and Lee (2011) conducted a study on LVR which is thinly surfaced asphalt pavement using a three dimensional finite element method. They investigated the surface deflection and subgrade strain under various pavement thicknesses, traffic loads, and materials properties. Albuquerque and Nunez (2011) developed a roughness prediction model using International Roughness Index (IRI) for low volume asphalt pavement networks in northeast Brazil. The prediction model was developed using environmental conditions, traffic volume, and bearing capacity. The predicted IRI values were matched well with the measured IRI compared to the predicted IRI model for high volume

roadway. Berthelot et al. (2011) used Ground Penetration Radar (GPR) and Heavy Weight Deflectometer (HWD) to assess the structural asset value of LVR networks in Saskatchewan, Canada. They use the GPR to find the trapped moisture in granular base at the preconstruction state, and use the HWD to assess structural condition of LVRs at the post construction stage. Based on these accurate structural performance data, the financial resources have been effectively allocated. Ahmad et al. (2007) conducted a research on the privatization of LVR maintenance owned by federal government in Malaysia. Under the privatization contract, the private pavement company performed periodic, routine, and emergency maintenance for all roads. The maintenance program includes pavement condition data collection, maintenance strategy, materials selection, and maintenance technology. De Soliminihac et al. (2007) studied different maintenance policies on the impact of asset value of unpaved LVR networks in Chile. The analysis was conducted under different budgetary conditions using by the HDM4 program. They concluded that medium levels of financial resource investment were needed to keep the asset value as same and a small increase of initial asset value of a LVR network could be achieved through high levels of financial resource investment. Due to extremely limited budget and resource allocation for M&R especially in rural areas, decision support tools must be

*Member, Associate Professor, Dept. of Civil Engineering, Kunsan National University, Kunsan, Korea (Corresponding Author, E-mail: [email protected]) **Research Engineer, Dept. of Civil and Environmental Engineering, The University of Texas at San Antonio, San Antonio, Texas, USA (Email: [email protected]) − 1630 −

Simple Methods for Approximate Estimation of Rehabilitation of Low Volume Roads

employed to determine the most cost-effective and efficient maintenance and rehabilitation practices to aid the highway agencies in the planning, budgeting, and selection of priority sections for M&R. For the past decades highway engineers have introduced specified standards for the road condition named service levels. The methods presented in this paper are used to select the road from the candidates that have the highest priority to be maintained or rehabilitated and with the aim to successfully define best rehabilitation practices and optimize the minimal budget. This paper presents two approximate methods in order to assist decision makers with the estimation of number and cost of pavement maintenance and rehabilitation sections based on the Highway Present Condition Index (HPCI) and trend of rehabilitation history based on the intervals of M&R periods for LVR networks typically managed by agencies with extremely limited resources, and thus, must employ the most cost-effective and efficient maintenance and rehabilitation practices in order to remain serviceable.

or expected to carry 25,000 Equivalent Single Axle Loads (ESALs) per year or 68 ESALs per day or less on average. The PMS database was then filtered to evaluate only low-volume pavement sections based on this classification as shown in Table 1, in which a total of about 628 LVRs were analyzed with subtotal of 328 sections classified as new pavements and 300 sections

2. Korea Pavement Management Data The Pavement Management System (PMS) in South Korea provides a rich data set from which to investigate maintenance and rehabilitation practices for high volume roads, however, data on low-volume pavements have not been managed well. The low volume road pavements, many of which have lasted in excess of 25 years, are in relatively good condition and are typically subject only to periodic maintenance or rehabilitation treatments every 8 to 20 years. This investigation seeks to determine the common M&R practices in the management of low-volume pavements based on long term historical data and short term highway survey. A four year PMS data gathered and surveyed in the year 2004 to 2008 from a number of flexible pavement sections in various highways in South Korea was used in this investigation. The relevant flexible pavement distress parameters from the PMS database such as traffic volume, number of lanes, maintenance and rehabilitation history, fatigue cracking, rut depth, pavement roughness, construction year and start of service, and other pavement structure information were analyzed based on their statistical and frequency distribution. According to the American Association of State Highway and Transportation Officials (AASHTO) (AASHTO, 1993) and Washington State Department of Transportation (WSDOT, 2001) definitions, low-volume pavements are defined as either carrying

Fig. 1. Frequency Distribution of Overlay Maintenance Periods in the PMS: (a) 1st Overlay, (b) 2nd Overlay, and (c) 3rd Overlay

Table 1. Low-volume Road Sections Filtered from the Year 2004-2008 Pavement Survey PMS Database ESAL 0-15 15-30 30-45 45-68 Total

New 36 21 17 18 92

2004 Rehab 12 20 22 34 88

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Total 48 41 39 52 180

New 37 35 34 26 132

2006 Rehab 27 40 17 39 123

Total 64 75 51 65 255 − 1631 −

New 36 33 33 27 129

2007 Rehab 27 40 16 37 120

Total 63 73 49 64 249

New 35 22 25 22 104

2008 Rehab 20 28 10 31 89

Total 55 50 35 53 193

Dae-Wook Park and Jose Leo Mission

classified as rehabilitated pavements. Figure 1 shows the frequency distribution of overlay maintenance periods wherein it can be seen that the maximum and average interval of the succeeding overlays after previous rehabilitations have decreased. Among other factors, this decrease in the frequency of succeeding rehabilitations may be due to the gradual increase in volume of traffic with time and deteriorating condition of the sub-base with repeated overlays. Figure 2 shows the distress conditions of the pavement based on the four year highway survey. More than 70 percent of the pavements were characterized by a fatigue cracking of less than 5% (Fig. 2(a)), rutting of less than 10 mm (Fig. 2(b)), and an International Roughness Index (IRI) less than 3.0 (Fig. 2(c)). These damage ratings and distress data were then used to calculate the HPCI as described in the next

section.

3. Analysis of Rehabilitation Cost 3.1 Highway Present Condition Index Pavement deterioration models are means to forecast future condition of pavements such as pavement distress and roughness, which is an integral feature of any Pavement Management System (PMS). Among the many techniques for developing pavement deterioration models, such as straight line extrapolation, regression, mechanistic-empirical, polyno-mial constrained least square, S-shaped curve, probability distribution, and Markovian

Fig. 3. Distribution of PMS Data for HPCI of Surveyed Sections

Fig. 2. Distribution of PMS Data for Distress of Surveyed Sections: (a) Fatigue Cracking, (b) Rutting, (c) Roughness

Fig. 4. Scatter Plot of HPCI Against Pavement Service Life and Linear Regression and Correlation Model: (a) New Pavement, (b) Rehabilitated Pavement

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Simple Methods for Approximate Estimation of Rehabilitation of Low Volume Roads

(Shahin, 1994), this study uses the equation of the HPCI by the Korea Highway Corporation (KICT, 2008) that is given as:

Table 2. HPCI Criteria for Maintenance and Rehabilitation (KICT 2008) Maintenance Type No Maintenance Surface Treatment Rehabilitation

HPCI = 4.564 − 0.348*IRI–0.36*RUT − 0.01(TC+AREA)**0.5 (1)

where, AREA = IRI = RUT = TC =

Area of alligator cracking and patching (m2/500 m). International Roughness Index (m/km), Rut Depth (cm), Thermal Cracking (m/500 m),

Figure 3 shows the distribution of HPCI of the low-volume road sections based on the four year highway survey, in which more than about 60 percent of the pavements were characterized by an HPCI greater than 3.0. These distributions of the HPCI were plotted and correlated against the pavement service life wherein an approximate linear relation was established (Fig. 4). The average slope of the linear regression determines the rate of deterioration of the highway pavement condition per year. Using this slope, it would then be possible to develop a linear deterioration model from the start of the service life once the initial HPCI values are known. The initial HPCI values can be approximated using Eq. (1) based solely on the average initial IRI after construction or rehabilitation and assuming that the damage and distress terms (RUT, TC, AREA) are initially zero. Based on the average initial IRI evaluated for newly constructed and rehabilitated pavements, the following linear deterioration model of the HPCI against the pavement service life (x) is obtained: For new pavements with an average initial IRI = 1.2 after construction: HPCI = 4.150 − 0.051x

(2)

For rehabilitated pavements with an average initial IRI = 1.5 after rehabilitation: HPCI = 4.042 − 0.084x

average rate of deterioration condition of the pavement per year. The initial values of the HPCI in the regression model are also seen to be in accordance with measured and average HPCI after the new construction or rehabilitation of pavements. Using Eqs. (2) and (3), an estimation method for M&R can be established based on HPCI for the approximate determination of rehabilitation needed pavement sections and cost as shown in Fig. 5. From Fig. 5, the variable ‘a’ is used to define the criteria in which M&R is to be performed as shown in Table 2 (KICT, 2008), and ‘f’ is the factor used to describe the new state of the pavement after M&R process based on the initial HPCI at start of service as described in detail by Park et al. (2009). 3.2 Trends of Rehabilitation History The prior condition of rehabilitated pavements is that they must had experienced an intolerable distress, damage, or deterioration that was already below a service level, minimum criteria, or acceptable standards for serviceability, and in which M&R was required and implemented. Without the use of any pavement deterioration models, another method for the evaluation of pavement maintenance schedule and costs for low-volume roads is presented in this section that is simply based on the trends of rehabilitation history that was implemented for each pavement section in the past. The basic relevant data is the interval of number of years in which rehabilitation and maintenance Table 3. M&R Criteria for Rehabilitation Period

(3) M&R

Equations (2) and (3) are equations of a straight line of the form y = b − mx, in which x = pavement service life, y = HPCI, b is the y-intercept or the initial HPCI values at start of service life (x = 0) that is determined from the trend line model of the linear regression, and the negative slope m of the line denotes the

Fig. 5. M&R Estimation Method based on HPCI (Lee et al., 2013) Vol. 17, No. 7 / November 2013

HPCI Criteria 3.5 3.0 2.5

1st Overlay 2nd Overlay 3rd Overlay

Rehabilitation period (years) Maximum Average Frequent 19 11 10 12 7 7 10 5 5

Fig. 6. Rehabilitation Overlay Periods based on Maximum, Average, and Frequency of Number of Years

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Dae-Wook Park and Jose Leo Mission

Fig. 7. M&R Estimation Method based on Trends of Rehabilitation History

have been performed on any pavement. Using the frequency intervals of the histories of rehabilitation periods from the PMS database - the criteria for maximum, average, and most frequent number of years for the interval of M&R period were analyzed and determined for the low-volume pavement data as shown in Table 3 and Fig. 6. A statistical evaluation was made to determine the interval of rehabilitation periods for the first, second, and third rehabilitation overlay based on the historical data of rehabilitation practice that was implemented in the past. Based on this information, it would be possible to make an approximate prediction of the probable length of pavement service life x at which the next rehabilitation schedule may be recommended by following the same statistical trend. Figure 7 shows the rehabilitation estimation method based on trends of rehabilitation history using the criteria presented in Table 3 and Fig. 6. As shown in Fig. 6, the rehabilitation interval periods decreases abruptly after the first rehabilitation and gradually decreases after the second and third overlay, indicating an increased frequency of M&R periods with repeated rehabilitation. In implementing the method shown in Fig. 7, the rehabilitation criteria are maintained after the third overlay as used in this study for the estimation for the number of rehabilitation needed sections and costs as demonstrated in the next section. In other words, succeeding rehabilitations after the third overlay will be at fixed intervals of time assuming that all the other factors contributing to pavement distress remain constant for the specific M&R time frame.

Fig. 8. Estimated M&R Needed Pavement Section at Different Strategies

4. Analysis Results A sample of low-volume pavement sections filtered from the PMS database that were surveyed in the year 2008 was analyzed to predict the probable maintenance schedule and estimate the total M&R cost for each year in the next 10-year maintenance period. The computed HPCI for each pavement during the year 2008 were used as the initial values or y-intercept b in Eqs. (2) and (3), and the criteria shown in Table 2 were applied for each section as the service life x was increased using the method based on HPCI as shown in Fig. 5. The factor f=1.0 was used to describe the newly rehabilitated state of the pavements after rehabilitation. Fig. 9 shows the distribution of predicted number

Fig. 9. Estimated Pavement M&R Costs at Different Strategies

of M&R needed sections at different M&R application strategies. As shown in Fig. 8, the number of no needed maintenance sections is decreased and the number of M&R needed sections is increased as M&R applied period becomes longer. The results indicate that M&R applications at proper time are needed to maintain overall pavements in good condition. Expected M&R

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Simple Methods for Approximate Estimation of Rehabilitation of Low Volume Roads

Fig. 11. Cumulative Prediction based on Rehabilitation Period: (a) Rehabilitation Needed Section, (b) Rehabilitation Cost Fig. 10. Cumulative Prediction based on HPCI: (a) Rehabilitation Needed Section, (b) Rehabilitation Cost

costs were predicted as shown in Fig. 9. The costs of surface treatment and rehabilitation overlay were assumed as 20 million Korean Won (KRW) per kilometer (US $11,200 KRW) and 50 million KRW, respectively. The inflation rate of construction costs was assumed as three percent per year. As shown in Fig. 9, the M&R costs were increased significantly as M&R applied period becomes longer. The results indicate that timely maintenance and rehabilitation are more economical compared to the costs when the M&R are delayed. Fig. 10 shows the predicted cumulative number of rehabilitation required sections and cost based on HPCI in a 10-year rehabilitation period. The number of rehabilitation required sections for each year is determined from the difference between the cumulative number of sections of the current and previous year. Once certain candidates for rehabilitation are being determined for each year from the low-volume road database, an approximate total annual cost of rehabilitation can be made to estimate the total amounts to be allocated for pavement rehabilitation as shown in Fig. 10(b). As shown in Fig. 10, when the HPCI criteria for rehabilitation is reduced to Vol. 17, No. 7 / November 2013

a lower serviceability level, lesser number of rehabilitation required pavement sections and costs are accumulated in the succeeding years. Thus in using this method, the determination of the appropriate HPCI criteria (Table 1) and serviceability level for rehabilitation is a significant factor in optimizing pavement rehabilitation costs. Similarly, the criteria shown in Table 2 or Fig. 5 were applied using the method based on rehabilitation period as shown in Fig. 6. The total number of rehabilitation overlay needed sections for each year was predicted by assuming a constant criterion for rehabilitation after the third overlay. Fig. 11 shows the predicted cumulative number of rehabilitation required sections and cost based on rehabilitation period. As expected and shown in Fig. 11, when rehabilitation is performed at longer time intervals, the maximum periods of rehabilitation gives the least number of rehabilitation sections and costs compared to that when rehabilitation is done at more frequent or average intervals. Thus, based on the trend of rehabilitation history, an appropriate timing of rehabilitation period interval has to be evaluated in order to optimize M&R costs, which may require an estimation of the future serviceability level such as HPCI method to complement the prediction and in order to prevent conservative estimates.

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Dae-Wook Park and Jose Leo Mission

5. Conclusions

Acknowledgements

Maintenance and rehabilitation criteria and policies as well as decision support tools for LVRs that comprise majority of a country’s highways have to be distinct and separate from that of high volume roads. Since vehicle traffic is major contributing factor in pavement distress and damage, the M&R practices applied for high volume roads may not be entirely applicable for low-volume pavements. This study presents two methods for the approximate prediction and estimation of rehabilitation overlay based on HPCI and trend of rehabilitation overlay periods that is applied to LVRs. The results indicate that M&R applications at proper time are needed to maintain overall pavements in good condition. Timely maintenance and rehabilitation are also found to be more economical compared to the costs when the M&R are delayed. The results of the study have also shown that economical costs for M&R must be viewed cumulatively in a specific rehabilitation time frame compared to a yearly evaluation of individual costs. In particular, it has been shown that lower criteria of HPCI for rehabilitation within the serviceable level, although predicting higher individual costs at the later times, are still found to be the most economical option compared to service level criteria with higher HPCI values. Thus in using this method, the determination of the appropriate HPCI criteria and serviceability level for rehabilitation is a significant factor in optimizing pavement rehabilitation costs. Similar trend of results are found with a criteria for rehabilitation based on maximum periods of rehabilitation, which showed a more economical choice compared to that when average and frequent periods of rehabilitations are made. Therefore, based on the trend of rehabilitation history, an appropriate period of rehabilitation interval has to be evaluated in order to optimize M&R costs, which may require an estimation of the future serviceability level such as HPCI method to complement the prediction and in order to prevent conservative estimates. The two methods presented in this study would serve as a decisionmaking tool for identifying, prioritizing, scheduling, budgeting, and determining the best strategies for rehabilitation works that is focused primarily on low-volume pavements. The two approximate methods presented may be regarded as tools that would also guide and aid the highway agencies for planning and future estimation and prediction of the serviceability condition of pavements that would optimized their limited budget allocations especially for low volume roads.

This work was supported by the National Research Foundation (NRF) of Korea Grant funded by the Ministry of Education (NRF-2010-013-D00075) and by Kunsan National University (KNU) in Korea. The authors would like to express thank to the NRF and KNU.

References Ahmad, T., Ahmad, J., and Hossain, M. (2007). “Privatization of lowvolume-road maintenance management: Malaysian experience.” Transportation Research Record, No. 1989, pp. 281-289. Albuquerque, F. and Nunez, W. (2011). “Development of roughness prediction models for low-volume road networks in northeast brazil.” Transportation Research Record, No. 2205, pp. 198-205. American Association of State Highway and Transportation Officials (AASHTO). (1993). AASHTO guide for design of pavement structures, American Association of State Highway and Transportation Officials. Washington, D.C., USA. Berthelot, C., Podborochynski, D., Anthony, A., and Marjerison, B. (2011). “Mechanistic-based nondestructive structural asset management testing to optimize low-volume road structural upgrades.” Transportation Research Record, No. 2205, pp. 173-180. De Solimihac, H., Hidalgo, P., and Chamorro, A. (2007). “Asset valuation of low-volume road networks: Application to Chilean unpaved roads.” Transportation Research Record, No. 1989, pp. 281-289. Kim, K. and Lee, H. (2011). “Study on nonlinear pavement responses of low volume roadways subject to wheel loads.” Journal of Civil and Engineering and Management, Vol. 17, Issue 1, pp. 45-54. Korea Institute of Construction Technology (KICT). (2008). Final report of the national highway pavement management system, Report No. KICT-2008-011, Korea Institute of Construction Technology (KICT), Korea. Lee, S. T., Park, D. W., and Mission, J. (2013). “Estimation of pavement rehabilitation cost using pavement management data.” Structure and Infrastructure Engineering, Vol. 9, No. 5, pp. 458-464. Muench, S. T., White, G. C., Mahoney, J. P., Sivaneswaran, N., and Pierce, L. M. (2005). “Maintenance and rehabilitation of low-volume pavements in Washington State.” Proc. of the 84th Annual Conference of Transportation Research Board, Washington, D.C., USA. Shahin, M. Y. (1994). Pavement management for airport, roads, and parking lots, Chapman & Hall, New York, USA. Washington State Department of Transportation (WSDOT, 2001). Key facts: A summary of transportation information. http://www.wsdot.wa.gov/KeyFacts/default.htm, WSDOT Finance and Administration Service Center, Olympia, WA.

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