Art Chaovalitwongse, W; Wang, W; Williams, T P and Chaovalitwongse, P (2012) Data mining framework to optimize the bid selection policy for competitively bid highway construction projects. Journal of Construction Engineering and Management, 138(2), pp. 277-286. ISSN 0733-9364
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bidding; construction projects; data mining; neural networks |
| Index terms: | change order, cost overrun, owner, regression model, data mining, construction industry, estimate, bidder, highway construction, project cost, neural network, competitive bidding, United States, bidding, construction project, cost increase |
| Subjects: | bidding, data science, artificial intelligence, financial and cost management, industry analysis, statistical analysis, sociology, production management, contractual condition, Geography, economics, civil engineering |
| Topics: | Digital Applications, Contract Administration, Cost Management, Engineering Principles, Geographical Context, Project Management, Research Practice, Stakeholder Management, Procurement |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here