Research & Projects
Research & Projects
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The Build America Center (BAC) will mobilize the use of innovative finance, funding, and project delivery solutions to foster new approaches to transportation infrastructure development projects. In collaboration with the U.S. Department of Transportation, the BAC will lead cutting-edge research, deliver innovative training, and provide customized technical assistance in support of the Implementation of the Bipartisan Infrastructure Law.
Recent Study
Recent Study
Exploring the Use of Machine Learning in Enhancing Bidding Decisions for Construction Projects
Exploring the Use of Machine Learning in Enhancing Bidding Decisions for Construction Projects
This study addresses the critical need to enhance competitive bidding strategies in construction by revisiting Friedman's 1956 bidding model and incorporating Ioannou's revised equations to improve predictive accuracy. The research fills a significant gap in integrating machine learning techniques into bidding theory, which offers a data-driven approach to optimize bid decision-making. Using synthetic data, logistic regression served as a baseline model, while Random Forest classifiers outperformed with 98% accuracy by addressing class imbalance and effectively capturing the non-linear relationships among key variables, such as reserve price and bid-to-cost ratio. The findings revealed that machine learning models could simplify complex bidding theories and provide contractors with actionable insights, supporting bid or no-bid decisions. However, reliance on synthetic data limits the generalizability of these results. Future work should focus on validating the proposed models using real-world bidding datasets and exploring advanced techniques, such as ensemble methods, to enhance predictive performance. This study underscores the potential of machine learning to transform traditional bidding practices, which oGers both theoretical advancements and practical implications for construction management.
List of Publications:
Qadri H. Shaheen, Suleiman A. Ashur, Huthaifah I. Ashqar (2025) “Exploring the Use of Machine Learning in Enhancing Bidding Decisions for Construction Projects”, DFBI Conferences: Vol. XXXX Article XXX.
Shaheen, Qadri H.; Abu-Eisheh, Sameer; Shaheen, Hafez; and Berghorn, George (2025) "Addressing Urban Blight for Cities Under Uncertain Conditions, Analysis within UN SDG 11,"CIB Conferences: Vol. 1 Article 401.DOI: https://doi.org/10.7771/3067-4883.1093
H. I. Ashqar, Q. H. Q. Shaheen, S. A. Ashur and H. A. Rakha, "Impact of risk factors on work zone crashes using logistic models and Random Forest," 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 2021, pp. 1815-1820, doi: https://doi.org/10.1109/ITSC48978.2021.9564405
Q.H. Shaheen, S.A. Ashur, Impact of Michigan's Highway Construction Work on Traffic Crashes Rates. The 2019 Association of Technology, Management and Applied Engineering (ATMAE) Conference, November 6-8, 2018 at the Sheraton Charlotte in Charlotte, NC, USA. ATMAE Conference Proceedings
Q.H. Shaheen, "Impact of Highway Work Zones on Traffic Crashes: A Case Study in Michigan" (2018). Master's Theses and Doctoral Dissertations. 950. https://commons.emich.edu/theses/950
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