Reducing Data-Collection Efforts for Conceptual Cost Estimating at a Highway Agency
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 11
Abstract
Data-driven models using historical project attributes to estimate future construction costs, such as multiple-regression analysis and artificial neural networks are both proven techniques that highway agencies could adopt for conceptual cost estimating. This research found literature using those techniques has been solely focused on estimating model performance with little to no attention to the level of effort required to conduct the conceptual estimate. It is commonly believed using more input data enhances estimate accuracy. However, this paper finds for the highway agency studied that using more input variables than necessary in the conceptual estimate does not improve estimate accuracy. Conceptual estimates using the minimum amount of input data to produce an estimate with a reasonable level of confidence is more cost effective. This paper quantifies the effort expended to undertake conceptual estimates using data from a highway agency and concludes that input variables that have a large influence on the final predicted cost and require a low amount of effort are desired in data-driven conceptual cost-estimating models for the agency studied.
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Acknowledgments
The authors would like to acknowledge Montana Department of Transportation for furnishing the data, interviews and assisting with the survey requirements for this research. We are also grateful for their continued support, especially the assistance of Lesly Tribelhorn and Kris Christensen.
References
AASHTO. (2013). “Practical guide to cost estimating.” Washington, DC.
Al-Tabtabai, H., Alex, A. P., and Tantash, M. (1999). “Preliminary cost estimation of highway construction using neural networks.” Cost Eng., 41(3), 19–24.
Anderson, S., Molenaar, K., and Schexnayder, C. (2007). “Guidance for cost estimation and management for highway projects during planning, programming and preconstruction.”, National Cooperative Highway Research Program (NCHRP), Transportation Research Board of the National Academics, Washington, DC.
Bell, L. C., and Bozai, G. A. (1987). “Preliminary cost estimating for highway construction projects.” AACE Trans., C6.1–C6.4.
Bode, J. (2000). “Neural networks for cost estimation: Simulations and pilot application.” Int. J. Prod. Res., 38(6), 1231–1254.
Byrnes, J. E. (2002). “Best practices for highway project cost estimating.” M.S. thesis, Arizona State Univ., Mesa, AZ.
Creese, R. C., and Li, L. (1995). “Cost estimation of timber bridges using neural networks.” Cost Eng., 37(5), 17–22.
Danielsson, P.-E. (1980). “Euclidean distance mapping.” Comput. Graphics Image Process., 14(3), 227–248.
Elhag, T. M. S., and Boussabaine, A. H. (1998). “An artificial neural system for cost estimation of construction projects.” Proc., 14th ARCOM Annual Conf., Vol. 1, Association of Researchers in Construction Management, Univ. of Reading, Reading, U.K., 219–226.
Emsley, M. W., Lowe, D. J, Duff, A. R., Harding, A., and Hickson, A. (2002) “Data modelling and the application of a neural network approach to the prediction of total construction costs.” Constr. Manage. Econ., 20(6), 465–472.
FHWA (Federal Highway Administration). (2015). “Fact sheets on highway provisions—Statewide planning.” 〈http://www.fhwa.dot.gov/safetealu/factsheets/statewide.htm〉 (Aug. 17, 2015).
Fink, A. (2009). How to conduct surveys: A step-by-step guide, 4th Ed., SAGE Publications, Thousand Oaks, CA, 1–125.
Flyvbjerg, B., Skamris Holm, M., and Buhl, S. (2002). “Underestimating costs in public works projects: Error or lie?” J. Am. Plann. Assoc., 68(3), 279–295.
Fowler, F. J. (2009). Survey research methods, 4th Ed., SAGE Publications, Thousand Oaks, CA, 1–199.
Gransberg, D. D., Lopez del Puerto, C., and Humphrey, D. (2007). “Relating cost growth from the initial estimate to design fee for transportation projects.” J. Constr. Eng. Manage., 404–408.
Gransberg, D. D., and Riemer, C. (2009). “Impact of inaccurate engineer’s estimated quantities on unit price contracts.” J. Constr. Eng. Manage., 1138–1145.
Gunaydin, H. M., and Dogan, S. Z., (2004). “A neural network approach for early cost estimation of structural systems of buildings.” Int. J. Project Manage., 22(7), 595–602.
Gunduz, M., Ugur, L. O., and Ozturk, E. (2011). “Parametric cost estimation system for light rail transit and metro trackworks.” Expert Syst. Appl., 38(3), 2873–2877.
Hegazy, T., and Ayed, A., (1998). “Neural network model for parametric cost estimation of highway projects.” J. Constr. Eng. Manage., 210–218.
Kim, G.-H., An, S.-H., and Kang, K.-I. (2004). “Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning.” Build. Environ., 39(10), 1235–1242.
Kim, H., Seo, Y., and Hyun, C., (2012). “A hybrid conceptual cost estimating model for large building projects.” Autom. Constr., 25, 72–81.
Lowe, D. J., Emsley, M. W., and Harding, A. (2006). “Predicting construction cost using multiple regression techniques.” J. Constr. Eng. Manage., 750–758.
Mahamid, I. (2011). “Early cost estimating for road construction projects using multiple regression techniques.” Australas. J. Constr. Econ. Build., 11(4), 87–101.
Minassian, V. K., and Jergeas, G. F. (2009). “A prototype risk analysis for determining contingency using approximate reasoning method.” Cost Eng., 51(1), 26–33.
Moselhi, O., Hegazy, T., and Fazio, P., (1992). “Potential applications of neural networks in construction.” Can. J. Civ. Eng., 19(3), 521–529.
Moselhi, O., and Siqueira, I., (1998). “Neural networks for cost estimating of structural steel buildings.” AACE Int. Trans., 6.1–6.4.
Petroutsatou, C., Lambropoulos, S., and Pantouvakis, J.-P. (2006). “Road tunnel early cost estimates using multiple regression analysis.” Oper. Res. An Int. J., 6(3), 311–322.
Petroutsatou, K., Georgopoulos, E., Lambropoulos, E., and Pantouvakis, J.-P. (2012). “Early cost estimating of road tunnel construction using neural networks.” J. Constr. Eng. Manage., 679–687.
Pewdum, W., Rujirayanyong, T., and Sooksatra, V., (2009). “Forecasting final budget and duration of highway construction projects.” Eng. Constr. Archit. Manage., 16(6), 544–557.
Rueda-Benavides, J. A., and Gransberg, D. D. (2014). “Indefinite delivery/indefinite quantity contracting: A case study analysis.” Transp. Res. Rec., 2408, 17–25.
Sanders, S. R., Maxwell, R. R., and Glagola, C. R. (1992). “Preliminary estimating models for infrastructure projects.” Cost Eng., 34(8), 7–13.
Schexnayder, C. J., Weber, S. L., and Fiori, C. (2003). “NCHRP synthesis of highway practice: Project cost estimating.” Transportation Research Board of the National Academics, Washington, DC.
Setyawati, B. R., Sahirman, S., and Creese, R. C. (2002). “Neural networks for cost estimation.” AACE Int. Trans., 13.1–13.9.
Smith, A. E., and Mason, A. K. (1997). “Cost estimation predictive modeling: Regression versus neural network.” Eng. Econ., 42(2), 137–161.
Turochy, R. E., Hoel, L. A., and Doty, R. S. (2001). “Highway project cost estimating methods used in the planning stage of project development.” Virginia Transportation Research Council, 1–290.
Verlinden, B., Duflou, J. R., Collin, P., and Cattrysse, D. (2008). “Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study.” Int. J. Prod. Econ., 111(2), 484–492.
Walczak, S. (2001). “An empirical analysis of data requirements for financial forecasting with neural networks.” J. Manage. Inf. Syst., 17(4), 203–222.
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© 2016 American Society of Civil Engineers.
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Received: Nov 13, 2015
Accepted: Mar 3, 2016
Published online: May 5, 2016
Discussion open until: Oct 5, 2016
Published in print: Nov 1, 2016
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