Construction projects typically are not withdrawn after going into the competitive bidding process. The decision of contracting authorities regarding which projects will proceed to the bidding stage depends, in part, upon the early estimates of probable cost. Efforts are made to make this estimate as realistic as possible. Irrespective of the estimate of probable cost, the actual project cost is established by the amount of the winning bid.
This study analyzed historical bid data of street construction projects undertaken by the Public Works Department, Clark County, Nevada, from 1991 through 2006. The focus of this study was on utilizing statistical models to develop improved methodologies for predicting bid-item unit pricing and reducing variances resulting in large discrepancies between project estimates and actual bid-award amounts. A regression model was developed to improve predictions of actual project costs based on calculations using all bid items. The resulting models were incorporated into a database and integrated into a computer software program to facilitate the predictive process for future projects.
Timely maintenance of highways is very essential to create uninterrupted traffic flow that results in reduced traffic delays and operating costs. Federal as well as State governments are struggling to maintain highways due to budget limitations and lack of decision making tools that help to prioritize the highways that require immediate maintenance. A large number of software has been recently developed based on Geographical Information System (GIS) for the management of traffic, road conditions, and safety data. The majority of the software provides an interface between data and maps. This paper describes the process of developing a GIS-based maintenance management tool that assists in making a reliable and accurate decision regarding road maintenance and management. Factors like traffic counts, pavement conditions, maintenance costs, and alternative roadways available were taken into account in this tool to make a decision regarding prioritization of road maintenance. Synthetic data was used to test and verify the tool. The importance of the tool is discussed and recommendations regarding future research are provided.