Data Mining Framework to Optimize the Bid Selection Policy for Competitively Bid Highway Construction Projects

In competitive bidding in the United States, the lowest bid is frequently selected to perform the project. However, the lowest bidder may incur significant cost increases through change orders. For project owners to accurately estimate the actual project cost and to predict the bid that is close to the actual project cost, there is a need for new decision aids to analyze the bid patterns. In this paper, two neural network models, a classification model and a general regression model, were used as a method of selecting the bidder that submits the bid closest to the actual project cost. The empirical results suggest that for selected projects these models selected the bids that are closer to the actual project costs than the lowest bid. The outcome of this study addresses the issue of cost overrun, which is a very common problem in the construction industry.