Prediction of Financial Contingency for Asphalt Resurfacing Projects using Artificial Neural Networks
Publication: Journal of Construction Engineering and Management
Volume 138, Issue 1
Abstract
Historically, actual construction costs have tended to exceed initial cost estimates and budgets. Often this discrepancy is significant enough to cause problems such as depletions of budgets, disputes, and reductions in work quality. Cost contingency is an important element included in the base cost estimate to protect construction participants including owners, contractors, and architects from the risks associated with underestimating project cost estimates and overrunning cost budgets. Typically, project participants have simply calculated contingency as a fixed percentage of project cost in spite of the importance of contingency. The uniform application of this deterministic method to calculate contingency on the basis of project costs only is not appropriate for all construction projects. This paper identifies factors that influence contingency and proposes a new method for predicting the owner’s financial contingency on transportation construction projects using an artificial neural network (ANN)–based method. Asphalt resurfacing works among transportation projects sponsored by the Florida Department of Transportation (FDOT) completed from 2004–2006 are used for this study. The results show the viability of the ANN approach in the prediction of contingency. Accurate predictions of contingencies using this approach can help project administrators better manage contingency requirements on financing projects, allowing a more optimal usage of available project funds.
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References
Adeli, H., and Wu, M. (1998). “Regularization neural network for construction cost estimation.” J. Constr. Eng. Manage., 124(1), 18–24.
Ahuja, H. N., Dozzi, S. P., and Abourizk, S. M. (1994). Project management, 2nd Ed., Wiley, New York.
American Association of Cost Engineers (AACE) Risk Management Committee. (2000). “AACE International’s risk management dictionary.” Cost Eng., 42(4), 28–31.
Baccarini, D. (2004). “Accuracy in estimating project cost construction contingency: A statistical analysis.” COBRA 2004: Int. Construction Research Conf. of the Royal Institution of Chartered Surveyors, Royal Institution of Chartered Surveyors (RICS), London.
Bouabaz, M., and Hamami, M. (2008). “A cost estimation model for repair bridges based on artificial neural network.” Am. J. Appl. Sci., 5(4), 334–339.
Burroughs, S. E., and Juntima, G. (2004). Exploring techniques for contingency setting, AACE Int. Trans.
Dey, P., Tabucanon, M. T., and Ogunlana, S. O. (1994). “Planning for project control through risk analysis: A petroleum pipelaying project.” Int. J. Proj. Manage., 12(1), 23–33.
Federal Highway Administration (FHWA). (2007a). Contingency fund management for major projects, FHWA, Washington, DC.
Federal Highway Administration (FHWA). (2007b). Major project program cost estimating guidance, FHWA, Washington, DC.
Flood, I., and Kartam, N. (1994). “Neural networks in civil engineering I: Principles and understanding.” J. Comput. Civ. Eng., 8(2), 131–148.
Florida Department of Transportation (FDOT). (2002). Construction project administration manual, FDOT, Tallahassee, FL.
Flyvbjerg, B., Holm, M. S., and Buhl, S. (2002). “Underestimating costs in public works projects: Error or lie?” J. Am. Plann. Assoc., 68(3), 279–295.
Gunaydin, H. M., and Dogan, S. Z. (2004). “A neural network approach for early cost estimation of structural systems of buildings.”Int. J. Proj. Manage., 22(7), 595–602.
Haykin, S. (1994). Neural networks: A comprehensive foundation, Macmillan, New York.
Hegazy, T., Tully, S., and Marzouk, H. (1998). “A neural network approach for predicting the structural behavior of concrete slabs.” Can. J. Civ. Eng., 25(4), 668–677.
Karlsen, J., and Lereim, J. (2005). “Management of project contingency and allowance.” Cost Eng., 47(9), 24–29.
Mak, S., and Picken, D. (2000). “Using risk analysis to determine construction project contingencies.” J. Constr. Eng. Manage., 126(2), 130–136.
Massachusetts Institute of Technology (MIT). (1989). “DARPA neural network study.” Technical Report 840, MIT Lincoln Laboratory, Lexington, MA.
Molenaar, K. R. (2005). “Programmatic cost risk analysis for highway megaprojects.” J. Constr. Eng. Manage., 131(3), 343–353.
Patrascue, A. (1988). Construction cost engineering handbook, Marcel Dekker, New York.
Popescu, C. M., Phaobunjong, K., and Ovararin, N. (2003). Estimating building costs, Marcel Dekker, New York.
Project Management Institute (PMI). (2000). A guide to the project management body of knowledge, PMI, Newton Square, PA.
Sodikov, J. (2005). “Cost estimation of highway projects in developing countries: Artificial neural network approach.” J. Eastern Asia Soc. Transp. Stud., 6, 1036–1047.
Thompson, P. A., and Perry, J. G. (1992). Engineering construction risks: A guide to project risk analysis and risk management, Thomas Telford, London.
U.S. DOE. (1994). Cost guide, U.S. DOE, Washington, DC.
Wilmot, C. G., and Mei, B. (2005). “Neural network modeling of highway construction costs.” J. Constr. Eng. Manage., 131(7), 765–771.
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© 2012 American Society of Civil Engineers.
History
Received: Jun 14, 2010
Accepted: Apr 14, 2011
Published online: Apr 15, 2011
Published in print: Jan 1, 2012
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