Prediction of Financial Contingency for Asphalt Resurfacing Projects using Artificial Neural Networks

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.