ANN-based estimation model for the preconstruction cost of pavement rehabilitation projects

Abstract

To combat prevalent roadway aging, the California Department of Transportation (Caltrans) implements routine rehabilitation projects, which require cost estimation models for better management. Several models have been developed to estimate the construction costs of new highway projects; however, few have targeted the cost of pavement rehabilitation, and, of these, most have focused primarily on the construction phase. This study presents an artificial neural network (ANN) estimation model that predicts the cost of the preconstruction phases (predesign, design, and bidding) of pavement rehabilitation projects. The model was developed using 139 projects maintained by Caltrans over the past five years. Pareto principle and sensitivity analysis were used to identify five project items that contributed significantly to the model’s high estimation performance, which was demonstrated by a coefficient of multiple determination value (R 2) of 76.9%. Thus, the proposed model will allow Caltrans to properly estimate its budget and manage its performance during the preconstruction phases of pavement rehabilitation projects. Moreover, it overcomes many limitations of conventional preconstruction cost estimation models and will allow Caltrans to better manage up to $80B that may be spent on its preconstruction activities. Finally, the model provides a benchmarking tool for tracking and controlling expenses.