Description

Because the construction phase accounts for the majority of project costs for pavement rehabilitation projects, most research on infrastructure project cost estimating focuses on that phase, rather than on the preconstruction phases. Nevertheless, costs incurred prior to construction, referred to in this report as "preconstruction costs" are significant and worthy of consideration (See Section 2.1 of the report for a more detailed and precise definition of preconstruction). In the 20202021 fiscal year, for instance, the California Department of Transportation (Caltrans) spent more than $169 million on preconstruction work for pavement rehabilitation projects. This report presents the results of a study of preconstruction cost estimating for pavement rehabilitation projects undertaken by Caltrans. It uses data on the 139 pavement rehabilitation projects for which Caltrans opened bids in the five-year period from April 26, 2016 to May 11, 2021. A data set was developed that combined the preconstruction hours for each project with the primary bid items for the pavement rehabilitation projects. Two models were developed to estimate preconstruction hours from the bid items, one using an Artificial Neural Network (ANN) and the other a parametric exponential model developed using multiple regression. The models had coefficients of determination of 0.85 and 0.80, respectively. Tools were then developed to assist professional users in validating their preconstruction cost estimates using each of the models. CTC staff or Caltrans can use these tools to evaluate the reasonableness of the preconstruction estimate on an individual project, or on the sum of an entire biennial SHOPP pavement rehabilitation portfolio, in order to assure the most efficient use of infrastructure funding to best serve the community's transportation needs.

Publication Date

5-2023

Publication Type

Report

Topic

Transportation Finance

Digital Object Identifier

10.31979/mti.2023.2148

MTI Project

2148

Keywords

Cost estimating, Neural networks, Regression analysis, Planning and design, Pavements

Disciplines

Transportation | Transportation Engineering

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