Comparison between traditional and digital preliminary ... - Springer Link

Innov. Infrastruct. Solut. (2017)2:19 DOI 10.1007/s41062-017-0066-7

TECHNICAL NOTE

Comparison between traditional and digital preliminary cost-estimating approaches Valeria Valentini1 • Claudio Mirarchi1

•

Alberto Pavan1

Received: 30 December 2016 / Accepted: 19 May 2017 Ó Springer International Publishing Switzerland 2017

Abstract The definition of preliminary project cost estimates is one of the key aspects of investments in the real estate field. This kind of estimates plays a fundamental role in the decision-making process defining whether to proceed with the project. However, the definition of accurate estimates in preliminary phases is still an open issue and several researches have been developed to improve the performance of the process. Most of the researches are focused on the introduction of digital methods that are able to improve performance both in terms of precision and effort reduction. This paper is focused on the comparison between two traditional estimating methods used in the preliminary project phase and a digital method based on Building Information Modeling processes and instruments, to understand the impact of digital practices on the traditional estimating processes. Keywords Cost estimating BIM Decision making Digital processes

& Claudio Mirarchi [email protected] Valeria Valentini [email protected] Alberto Pavan [email protected] 1

Architecture, Built Environment and Construction Engineering Department (ABC), Politecnico di Milano, Via G. Ponzio, 31, 20133 Milano, Italy

Introduction The definition of preliminary project cost estimates is one of the key aspects of investments in the real estate field. This kind of estimates plays a fundamental role in the decision-making process defining whether to proceed or not with the project [1]. Furthermore, these first estimates constitute the basis for all the future cost-estimating procedures developed during the different phases of the project. Despite the huge efforts spent in research, estimation in a preliminary phase is still an open issue. In this direction, two main issues can be listed: first, preliminary cost estimates are often inaccurate with high variations between these estimates and the real cost of construction. Second, their development requires a significant amount of efforts [2]. This is directly connected to the characteristics of projects in early phases. Estimators must make many assumptions relying on limited information available in these phases with a consequent high degree of uncertainty [3]. In addition, estimations in the industry are often defined using relatively simple approaches both in estimates and in the detection of estimate errors. In most of the cases, estimators rely on historical costs data from the previous works or founded in dedicated historical databases. The estimation is based on the judicious selection of data through data sampling, evaluating project and activity characteristics between the previous projects and new projects [4]. This is inherently associated with evident difficulties connected to the complexity and uniqueness of the final construction industry products (buildings, infrastructures, etc.) [5]. The poor precision of the cost definition creates an imbalance between the prediction about inputs and outputs in the cash flow, making it difficult to effectively evaluate real estate investments. Nowadays, the construction field is undergoing a progressive change under

123

19

Page 2 of 8

the effect of technological developments. Building Information Modeling (BIM) represents, nowadays, the main point of integration between the digital processes and the construction field. There is no univocal definition of BIM [6]. A possible source is in [7], where BIM is defined as ‘‘the use of a shared digital representation of a built object (including buildings, bridges, roads, process plants, etc.) to facilitate design, construction, and operation processes to form a reliable basis for decisions’’. In the definition of preliminary cost estimates, there are discordant visions about the use of BIM. While several researches have demonstrated the positive effect of the use of digital instruments and processes including BIM, in other cases, the automation is obtained through different instruments and the introduction of BIM is postponed in a more advanced phase of the construction process [2]. In any case, thanks to the development of information technology instruments and applications, it is expected that the efficiency and accuracy of cost estimation can be greatly improved by implementing cost specifications in computer programs [8]. The introduction of digital instruments can also improve the process of evaluating different project solutions [9]. The huge efforts required in the definition of the traditional preliminary cost estimates can hinder the analysis of different project solutions limiting the decisionmaking process and the possibility to reach the best possible result. The use of automated processes can reduce the required efforts (especially in the evaluation of different solutions on the same project) and consequently allow a more easy exploration of different proposals. The objective of this paper is to compare different traditional cost-estimating methods with a digital-based costestimating method applicable to the schematic design phase. The aims are to identify strength and weakness of the different approaches and understand how the introduction of digital instruments can change the actual costestimating practices. The proposed method uses a BIM approach with a low definition of the project (preliminary stage) basing the estimation on masses and parameters defined in the model. The use of this kind of approach is not new, and for example, a building information modeling and ontology-based approach, defined for the schematic design phase, is proposed in [10]. Thus, the focus is not on the proposal of an innovative approach, but it is on the comparison between the traditional and digital approaches, understanding how preliminary cost estimates can be affected. The paper is organized as follows. Chapter 2 contains a brief presentation of different cost-estimating methods with a specific reference on the three methods applied in the research. Chapter 3 presents the research methodology. Chapter 4 is focused on the definition of the developed analysis and introduces the case study used for the analysis. Chapter 5 contains the results obtained

123

Innov. Infrastruct. Solut. (2017)2:19

applying the different methods and the comparison between them. Finally, Chapter 6 presents the conclusions of the work.

Cost-estimating methods With reference on the different existing cost-estimating methods, four main categories can be defined: statistical analysis of itemized costs, factor analysis models, timedependent cost trend projection, and integrated analysis of multi-objectives [4]. In the industry, the first method is the wider used because of its simplicity. The other groups are presented and analyzed in several research papers, but their use in the field is limited, even if is demonstrated the lack of performances in the application of comparative methods. This chapter is focused on a brief presentation of two methods traditionally used in preliminary estimates followed by the presentation of a method based on BIM processes and instruments. Are presented the unit cost method, the A.R.C.1 method, and the digital method (based on BIM processes). Unit cost method The unit cost method (or parametric cost method) is a comparative cost-estimating method. The estimating process provides four main passages. The first point is the individuation of a sample of buildings with functional characteristics, dimensional characteristics, technological characteristics, morphological characteristics, and context situations similar to those of the good being estimated. The second step is the definition of the characteristic dimensional parameter that could be the square meters of the building, the number of beds in the case of hospitals or hotel, etc. Follows the definition of the parametric cost of the sample calculated summing the values of construction costs of the sample divided by the sum of the characteristic parameter of the sample. The last step is the estimate that is obtained by multiplying the parametric unit value calculated in the third step by the value of the parameter referred to the good object of the estimation. In the practice, in absence of a significant sample of comparison is possible to use dedicated manuals, where are defined the unit cost of the characteristic geometrical parameter for several building types. This last case is the one analyzed in the paper, because it is widely used in the practice. In the industry, this estimation method is based on paper manuals and tables. The ease of use of this method has set it as the most common in the preliminary phase of the project. However,

1

Appre´ciation rapide des couˆts de construction.


there are several limitations connected to this kind of estimation: (a)

(b)

(c) (d)

The paper manual used for the definition of the cost of the parameter has a limited dimension and is not able to admit all the variants of the construction industry. In parallel are not defined methods to broaden the comparison on building types not included in the manual. Tables and data are developed on a local scale. The cost is estimated on a singular parameter and it is not possible comparing different project solutions that maintain constant characteristic parameter.

A.R.C. method The A.R.C. method was developed in the 50s by the ‘‘Centre Scientifique at Tecnique’’ in Paris. It is based on tables defining parameters and factors to be used in the estimation process. This method is defined for all the phases of the construction process and for its applicability are identified eight phases: (1) financing; (2) program; (3) plan; (4) sketch design; (5) concept design; (6) executive design; (7) evaluation of quantity and quality; and (8) determining incidences. In the first two phases, there is not geometry, while from the third one, there is a progressive increase in the definition of the geometry, starting from masses. This paper is mainly focused on this part of the method, because it is the one that can be compared to the unit cost method and to the digital method proposed. The method contextualizes the technical and financial aspects determining the optimal technical solution. The application of the method requires the individuation of elementary quantity grouped by affinity with an increasing detail about geometry and performance during the advancement of the process. This kind of approach allows a better precision on the evaluation of the cost that, in this case, is subdivided into different homogenous parts instead of a singular unit cost. On the other hand, the definition of groups does not require precise hypothesis on details that are not known in a preliminary stage, ensuring the applicability in early phases. The ability of comparing different project solutions is one of the main characteristics of the method [11] and with the ability to create a final estimate subdivided into different homogenous costs, which represent a clear break with the previous method. However, the A.R.C. is focused on the evaluation of residential buildings and the extension on different types of uses requires a huge effort in the definition of new usable price libraries. Furthermore, the method is applicable only in the case of new buildings and cannot be used in the case of renovation, change in intended uses, etc.

Page 3 of 8 19

Digital method The proposed digital method is based on the implementation of BIM processes through the integration of structured costs in the geometry given in a preliminary design phase. As known, a building information model is realized with an object-oriented software (BIM Authoring tools) and consists of parametric objects representing building components [12–14]. These objects are characterized by geometric and non-geometric attributes [15, 16]. The evolution of the information content during the different phases of the project is measured using LOD [17]. This acronym is nowadays commonly recognized as a level of development including geometric and non-geometric information associated with the objects. This is not a harmonized LOD scale, i.e., a globally shared definition for each level of development [17]. Furthermore, the evaluation of the information contained in the virtual objects that constitute the model generated using BIM Authoring tools must consider also the information that are linked to the objects from different sources (e.g., databases, devices, etc.) [18–20]. Thus, the implementation of a digital system that is able to provide cost evaluation in a preliminary phase requires the definition of a structured cost library that can be defined with different levels of integration, starting from element cost types and reaching the introduction of resources and activities. The cost library is commonly defined through a relational database that allows the connection between different cost voices creating a tree of cost decomposition. Each cost voice is linked to the associated object created in the model. To admit the estimation of projects at a preliminary stage, is required the integration of different factors and parameters that are able to identify and estimate the characteristics of the building that are not directly modeled and consequently must be obtained from the existing data (volume, wall surfaces, floor surfaces, etc.). At the end of the implementation, the system allows the modeling phase, where through the definition of masses and known details, is possible obtaining the final estimate, divided into the cost voices that have been defined for the specific building type.

Research methodology The research is developed following four main steps plus one. The first step is the selection of estimating methods. To define a useful comparison on the market, we selected two of the methods widely used in cost estimating in the schematic design phase. These two methods are then used as basis for the comparison with the one based on digital processes and instruments. The second step is the definition of the needed elements for each method. The selected

123

19

Page 4 of 8

methods are not directly comparable to each other due, for example, to the price library on which they are based, the effectively application on the case study, and so on. Thus, to obtain a usable comparison, we had to adjust in some part the methods. Defined the methods and adjusted the identified issues, the research includes the development of separated analysis on the case study, independently for each selected method. The last step is the comparison of the results obtained using the different methods. In addition to these main steps, there is another phase of the research that is focused on the identification of the usability of the different methods in the decision-making phase intended as design optioneering. One of the main possibility offered by the introduction of digital technology and processes like building information modeling is the anticipation of problems and analysis in the design phase. Thus, the definition of methods that are able to improve the performance of this practice is of great interest. The last step is then focused on the comparison between the different possibilities offered by the different methods in this regard.


price library was required. A second point is about the inability of the method in the evaluation of some particular aspects of the selected buildings. The main problem is about elements that are not uniformly distributed on vertical and/or horizontal spaces such as pillars and beams. Thus, for linear structural elements were defined different tables that are able to distribute a standard linear cost on a unit area to allow the applicability of the method also in the case of structure to beams and columns. About the digital method, the software selected for the application of the digital method is based on American standards. For this reason, a first calibration of all the parameters and the base units used in the system was needed to allow the final comparison. In the same way, the integration of Italian costs in the internal library of the software was required. This last step includes the introduction in the pre-structured database of costs provided by Italian pricelists coherent to the ones used in the application of the unit cost method and of the A.R.C. method. Case study

Analysis As stated in ‘‘Research methodolody’’ chapter, the research is divided into four main steps. The first one, related to the selection of the estimating method is presented in ‘‘Cost estimating methods’’ chapter, where are defined the methods used in the analysis developed. The applications reported in this chapter are then focused on the use of the three methods presented: the unit cost method, the A.R.C. method, and the digital method. The case study presented in ‘‘Case study’’ chapter and used in the development of the analysis is located in north Italy. Thus, for the unit cost method, is applied a manual commonly used in the industry containing cost for building types [21]. The A.R.C. is adapted for the application on the Italian context, as illustrated in ‘‘Preparing the methods’’ chapter. The digital method is based on the implementation of a specific software called DProfiler developed by the Back Technology Ltd. that allows the definition of a digital model of masses including parameters, factors, and a relational database for the definition of the project costs in the different phases of the process. Preparing the methods Due to the origin of the A.R.C. method, before the development of the analysis on the Italian context are required some adjustments. The main rectification is about the cost library originally defined for the method. The existing costs are old and referred to a French market. Thus, a redefinition of the original table through new costs based on Italian

123

To give a real parameter of comparison, the proposed analysis is based on an existing case study. This is used as basis for the application of the different cost-estimating methods using the existing design hypothesis. The case study, at the state of the art, is composed by five buildings (Fig. 1). There are three barns (Building type 2), a farmhouse (Building type 1), and a shed (Building type 3). The project defined for this complex includes the renovation of the farmhouse with the realization of co-working spaces on the ground floor and apartments on the upper floors. For the three barns, is provided the complete demolition and reconstruction, maintaining the same volume realizing residential and commercial spaces connected to the park environment. In the shed, actually used as repository, is provided the renovation with the introduction of a restaurant. Application Defined the case study, the estimating methods and the adjustments needed for their applicability (‘‘Preparing the methods’’) are possible defining the singular cost estimates for each building and each method. The A.R.C. method can be applied only on new buildings, and then, it is used for the estimation of the project hypothesis referred to the existing barns. The application of the A.R.C. method requires the deconstruction of the building through a working breakdown structure (WBS) that is used as basis for the definition of elements groups. Each element group is associated with the correspondent cost component provided by an Italian pricelist in [22] (the same used in the


Page 5 of 8 19

Fig. 1 Model developed about the case study

construction of the database defined in the digital method). The calculation is performed on a ‘‘reference cell’’ (e.g., an average apartment of the building) extending the evaluation to the number of cells identified. The unit cost method and the digital method are applicable both to new buildings and to renovations works, so they are applied on all the five buildings presented in the project. According to the destination of use of the buildings involved in the estimate, the reference unit cost is the square meter. The application of the unit cost method is based on the research of prices connected to building types that are similar to those in the project. The used source is an Italian unit cost list [21], where have been found the researched price sheets. About the digital method, the LOD of the objects included in the model is aligned with the known information in a preliminary project phase. Thus, objects are modelled with simple geometries and a low number of non-geometric information. In particular, non-geometric information is referred to element cost according to the objective of the model. Elements that in the real world are composed of several layers such as walls, slabs, etc. are modelled as a singular geometry that is associated with an assembly cost defined in the linked database. This cost is composed in the database through the association between the costs extracted from the pricelist in [22] and the supposed composition of the assembly. The obtained results and the comparison between the different methods are presented in Chapter 5.

Results Before presenting the results obtained applying the different methods, independently on the case study presented, it is possible to proceed with a first comparison between the main characteristics of the three selected methods. In Table 1, are reported the specifications of each methods in

terms of classification (which kind of estimating class is used in the method), price library (which is the price source on which the method base its estimates), and output (which is the expected output obtained applying the method2). As defined in Chapter 2.2, the A.R.C. method is applicable only on new constructions. For this reason, we need two kinds of comparisons, the first one including all the three methods applied on the three barns and a second on all the other buildings including only the unit cost and the BIM methods. In Table 2, are reported the results obtained applying the three methods (each one independently) on the project hypothesis referred to the existing barns. As shown in the lower part of the table, the cost calculated using the unit cost method is higher than the cost obtained using the digital method by about 33.2%. The lowering of the estimate value is commonly associated with the application of more precise estimating methods. The variance value found is near the evidences shown by other studies in which are compared methods based on the standard unit methods and methods based on a more accurate analysis [23]. Extending the comparison to the two buildings on which is hypothesized a renovation with a change in the intended use the results are divergent (Table 3). Analyzing the project about the farmhouse, there is a difference of about 50% with a higher value obtained using the unit cost method. Conversely, in the case of the shed, the value obtained using the unit cost method is lower and the gap is about 8%. These differences are probably due to the increase in the degree of uncertainty connected to renovation works. Renovation works are characterized by a strong deviation connected to the specific intervention 2

Obviously, for each method the final output is the estimate of the good. In this case, outputs are intended as the step before the final estimate that is the decomposition of the cost in the unit or quantity used for the estimate itself.

123

19

Page 6 of 8


Table 1 Comparison between the characteristics of the three methods Unit cost method

A.R.C. method

BIM method

Classification

Parametric costs

Elements and factors costs

Elements and factors costs

Price library

Building type library [21]

Element type library (Lombardy region) [22]

Element type library (Lombardy region) [22]

Output

Building cost per square meters

Building cell cost per square meters

Cost/quantity of materials, elements and buildings.

Table 2 Comparison between the three used methods on the project hypothesis referred to the existing barns

m2

BIM method €/m

2

A.R.C method 2

€

€/m

€/m2

€

Barn a

900.45

780.4

702,698.0

932.4

839,554.7

1,132.0

1,019,309.0

Barn b

722.88

808.2

584,263.1

1,109.3

801,891.7

1,210.5

875,019.0

Barn c

722.88

806.6

583,100.6

1,109.3

801,891.7

1,210.5

875,019.0

2346.20

797.1

1,870,061.7

1,041.4

2,443,338.0

1,180.3

2,769,347.9

Tot

68.9%a

82.4%d

100%

66.8%b

91.6%e

100%

c

91.6%f

100%

66.6% a

Percentage ratio between cost values from the BIM method and from the Unit Cost method (Barn a)

b

Percentage ratio between cost values from the BIM method and from the Unit Cost method (Barn b)

c

Percentage ratio between cost values from the BIM method and from the Unit Cost method (Barn c)

d

Percentage ratio between cost values from the A.R.C. method and from the Unit Cost method (Barn a)

e

Percentage ratio between cost values from the A.R.C. method and from the Unit Cost method (Barn b)

f

Percentage ratio between cost values from the A.R.C. method and from the Unit Cost method (Barn c)

Table 3 Comparison between BIM and unit cost methods

m2

BIM method €/m2

Building_1 Farmhouse

Unit cost method €

€/m2

€

1,243.88

496

616,363

991.00

1,232,685

Building_2 Barn a

900.45

780

702,698

1,132.00

1,019,309

Building_2 Barn b

722.88

808

584,263

1,210.46

875,019

Building_2 Barn c

722.88

808

584,263

1,210.46

875,019

Building_3 Shed

469.51

1,869

877,362

1,726.00

810,374

according to many factors such as state of the existing building, reuse of existing part of the building, required integration, etc. Thus, the definition of comparisons with similar works (needed for the unit cost method) is characterized by a high degree of aleatory. The definition of a digital model that is able to control a wider number of variables permits a better integration with the project hypothesis especially in the case of renovation works, with a consequent increase in the precision of the estimate. Looking at the final point of this research (the ‘‘plus one’’ cited in chapter 3), we can analyze the cost decomposition underlined by the BIM method. Thanks to the inherent structure of the cost library (the linked database), the BIM method allows the decomposition of the final cost in different homogeneous cost groups. The level of deconstruction is

123

€

Unit cost method

Table 4 Estimation of the cost-geometry correlation Demolitions

25%

Excavations

24%

Structures (beams, pillars, etc.)

12%a

Horizontal elements On the ground Indoor

25% 25% 26%

Roof

25%

Vertical elements

21%

a b

Doors and windows

27%b

Walls

15%

minimum cost-geometry impact maximum cost-geometry impact


defined by the user and allows the control of elements that have a greater impact on the final cost. This kind of approach is particularly useful in decision-making processes and in the evaluation of different project hypotheses. We have tested this peculiarity in the comparison between the two dimension defined for the barns. In Table 4, are reported the results in terms of price variance obtained with a variation in the geometry of the barns (in particular, barn ‘‘a’’ is longer then barns ‘‘b’’ and ‘‘c’’). This kind of evaluation cannot be defined using the unit cost method, because it is based on a singular parameter of comparison (in our case, the square meters of the building). The A.R.C. method admits the decomposition of the final cost, but the evaluation is less accurate and less scalable. Furthermore, the method is focused on the selection of the best technology, but the exploration of variations in geometrical aspects requires a big effort. The introduction of digital processes and instruments allows a more precise evaluation of the project defining, where are the main cost sources and by this allowing the designer (or the investor) evaluating how variations on singular aspects of the project can impact on the final cost. Thanks to the inherent connection between geometry and cost library, the exploration of both technical and geometrical solutions require limited efforts. This is one of the key aspect obtainable introducing BIM in the early stage of the process that is the improvement of the ability of decisions thanks to automated processes of analysis (including estimation).

Conclusions The study has presented a comparison between the traditional estimating methods and a digital-based estimating method based on BIM. The definition of more details even in the traditional analysis, passing, for example, from the unit cost method to the A.R.C. method, shows how the first one tend to overestimate the good (from about 9 to 17%). However, the introduction of a more accurate method still based on the traditional practices requires more efforts and manual calculations. The introduction of digital-based processes shows an increasing precision in the definition of project costs allowing an easily estimation of different solutions on different aspects of the project, geometry, performance, etc. However, the first phase of the process is much more expensive and requires a big effort in the implementation of digital systems including processes and instruments. This study is developed using a dedicated software that collects in a singular instrument all the characteristics needed for the definition of a correct estimation in early phases using BIM methodology. The evolution of this research provides the extension of the comparison including instruments that are more general. DProfiler is not widespread in the European market; thus, there are needs of

Page 7 of 8 19

using instruments more easily available in the European construction industry. The objective is to evaluate the effectively obtainable benefits with non-dedicated instruments (still based on BIM) and the required efforts for the initial implementation of the system. Acknowledgement We would like to thank Back Technology Ltd. for its support in the development of the study.

References 1. Oberlender GD, Trost SM (2001) Predicting accuracy of early cost estimates based on estimate quality. J Constr Eng Manag 127(3):173–182 2. Garcı´a de Soto B, Adey BT, Fernando D (2016) A hybrid methodology to estimate construction material quantities at an early project phase. Int J Constr Manag 3599(December):1–32 3. Soutos M, Lowe DJ (2005) ProCost—towards a powerful early stage cost estimating tool. In: Proceedings of the international conference on computing in civil engineering. pp 1–12 4. Hwang S (2016) A Bayesian approach for forecasting errors of budget cost estimates. J Civ Eng Manag 22(2):178–186 5. Nam CH, Tatum CB (1988) Major characteristic of constructed products and resulting limitations of construction technology. Constr Manag Econ 6:133–148 6. Ciribini ALC, Bolpagni M, Oliveri E (2015) An innovative approach to e-public tendering based on model checking. Procedia Econ Financ 21(15):32–39 7. International Organization for Standardization, ISO 29481-2, Building information modelling—Information delivery manual— part 2: Interaction framework 8. Ma ZL, Wei ZH, Zhang XD (2013) Semi-automatic and specification-compliant cost estimation for tendering of building projects based on IFC data of design model. Autom Constr 30:126–135 9. Pavan A, Antisari CV, Mirarchi C, Rizzarda C (2017) Design optioneering and BIM: a digital revolution. Area 150:78–83 10. Lee SK, Kim KR, Yu JH (2014) BIM and ontology—based approach for building cost estimation. Autom Constr 41:96–105 11. Caruso di Spaccaforno A (1979) Valutazione economica delle scelte progettuali con il metodo A.R.C 12. Nicolle C, Cruz C (2010) Semantic building information model and multimedia for facility management. In: International conference on web information systems and technologies. Springer, Berlin, pp 14–29 13. Cerovsek T (2011) A review and outlook for a ‘Building Information Model (BIM)’: a multi-standpoint framework for technological development. Adv Eng Inform 25:224–244 14. Tang P, Huber D, Akinci B, Lipman R, Lytle A (2010) Automatic reconstruction of as-built building information models from laserscanned point clouds. Autom Constr 19:829–843 15. Eastman C, Teicholz P, Sacks R, Liston K (2011) BIM handbook: a guide to buildinginformation modeling for owners, managers, designers, engineers, and contractors, 2nd edn. John Wiley and Sons, Hoboken, NJ 16. Wong J, Yang J (2010) Research and application of building information modelling (bim) in the architecture, engineering and construction (AEC) industry: a review and direction for future research. In: Proceedings of the 6th international conference on innovation in architecture, engineering & construction (AEC). pp 356–365 17. Bolpagni M, Ciribini ALC (2016) The information modeling and the progression of data-driven projects. In: Proceedings CIB world

123

19

Page 8 of 8

building congress 2016, vol III. Building up business operations and their logic shaping materials and technologies. pp 296–307 18. Laakso M, Kiviniemi A (2012) The IFC standard—a review of history, development, and standardization. J Inf Technol Constr 17:134–161 19. Pauwels P, Zhang S, Lee YC (2017) Semantic web technologies in AEC industry: a literature overview. Autom Constr 73:145–165 20. To¨rma¨ S (2013) Semantic linking of building information models. In: Proceedings—2013 IEEE 7th international conference on semantic computing, ICSC 2013. pp 412–419

123

Innov. Infrastruct. Solut. (2017)2:19 21. CIAM (2014) Prezzi per tipologie edilizie 22. Lombardia R (2011) Prezziario delle Opere Pubbliche—Regione Lombardia 23. Bayram S, Ocal ME, Laptali Oral E, Atis CD (2015) Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey. J Civ Eng Manag 3730:11