Degree Name
MS (Master of Science)
Program
Engineering Technology
Date of Award
5-2026
Committee Chair or Co-Chairs
Dr. K. Joseph Shrestha
Committee Members
Dr. Mohammad Moin Uddin (Co-Chair), Dr. Arpita Nandi (Committee Member)
Abstract
To address the limitations of traditional planning-level cost estimating methods, such as reliance on statewide averages, heavy reliance on engineering judgment, and high levels of inaccuracy, this study developed two data-driven frameworks and corresponding tools. The first framework focused on resurfacing projects and used treatment-specific simple linear regression based on lane miles and geographic location. The second addressed a broader group of construction projects and used multiple linear regression with project length, route type, and right-of-way cost as key predictors. In both frameworks, the final estimates are computed by aggregating multiple estimates produced by county-, region-, and state-level models. The resurfacing framework produced more stable results, with an overall MAPE of 32.45% and MPE of -1%. The construction framework showed weaker performance, with an overall MAPE of 143.91% and MPE of 106.04%. Both frameworks produced estimates that generally align with industry standards for the expected accuracy of early-stage cost estimates.
Document Type
Thesis - unrestricted
Recommended Citation
Paul, Pritom, "A Machine Learning Framework for Early-Stage Cost Estimation of Transportation Projects" (2026). Electronic Theses and Dissertations. Paper 4697. https://dc.etsu.edu/etd/4697
Copyright
Copyright by the authors.
Included in
Civil Engineering Commons, Construction Engineering Commons, Construction Engineering and Management Commons
