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

Sang Choon Lhee · Raja R. A. Issa · Ian Flood

38 citations0/0 files storedJournal of Construction Engineering and Management

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Construction Project Management and Performance BIM and Construction Integration Life Cycle Costing Analysis

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

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2020Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase 2022Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves 2021Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions 2020Cost Performance and Cost Contingency during Project Execution: Comparing Client and Contractor Perspectives 2020Risk induced contingency cost modeling for power plant projects

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