Applying Machine Learning Technique to Improve Cost Estimating Process for Tennessee’s Transportation Projects

Additional Authors

Dr. Mohammad Moin Uddin, Professor, Department of Engineering, Engineering Technology, and Surveying, East Tennessee State University, Johnson City, TN.

Abstract

According to the 2021 America’s Infrastructure Report Card by the American Society of Civil Engineers, 43% of U.S. roadways are in poor or mediocre condition, costing drivers over $1,000 per year in wasted time and fuel. A key challenge is the inaccurate estimation of road repair and maintenance costs, which hinders efficient fund allocation. While extensive research exists on cost prediction for new construction, infrastructure improvement projects, such as bridge repairs and road rehabilitations, receive less attention despite their unique complexities. This research addresses that challenge by developing a Cost Prediction Tool using Excel. The tool is specifically designed to assist Tennessee Department of Transportation (TDOT) engineers in overcoming difficulties with cost estimation during the planning phase. An in-depth analysis was done on 11 years of road and bridge project data across different regions of Tennessee, and various factors like project length, route, work type, and location were considered for developing the tool. Machine learning models were developed to predict costs at the state, regional, and county levels for projects such as bridge repairs, safety improvements, and legislative programs. These models were validated with new data, and the validation results demonstrated strong performance in early project phases. A spreadsheet-based tool was then created to make cost estimation quick and easy. This tool will be particularly valuable in emergencies, such as natural disasters like Hurricane Helena, by helping TDOT to fast-track the approval and funding for urgent repairs, ensuring a faster response and resource allocation.

Start Time

16-4-2025 1:30 PM

End Time

16-4-2025 2:30 PM

Room Number

303

Presentation Type

Oral Presentation

Presentation Subtype

Grad/Comp Orals

Presentation Category

Science, Technology and Engineering

Faculty Mentor

K. Joseph Shrestha

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Apr 16th, 1:30 PM Apr 16th, 2:30 PM

Applying Machine Learning Technique to Improve Cost Estimating Process for Tennessee’s Transportation Projects

303

According to the 2021 America’s Infrastructure Report Card by the American Society of Civil Engineers, 43% of U.S. roadways are in poor or mediocre condition, costing drivers over $1,000 per year in wasted time and fuel. A key challenge is the inaccurate estimation of road repair and maintenance costs, which hinders efficient fund allocation. While extensive research exists on cost prediction for new construction, infrastructure improvement projects, such as bridge repairs and road rehabilitations, receive less attention despite their unique complexities. This research addresses that challenge by developing a Cost Prediction Tool using Excel. The tool is specifically designed to assist Tennessee Department of Transportation (TDOT) engineers in overcoming difficulties with cost estimation during the planning phase. An in-depth analysis was done on 11 years of road and bridge project data across different regions of Tennessee, and various factors like project length, route, work type, and location were considered for developing the tool. Machine learning models were developed to predict costs at the state, regional, and county levels for projects such as bridge repairs, safety improvements, and legislative programs. These models were validated with new data, and the validation results demonstrated strong performance in early project phases. A spreadsheet-based tool was then created to make cost estimation quick and easy. This tool will be particularly valuable in emergencies, such as natural disasters like Hurricane Helena, by helping TDOT to fast-track the approval and funding for urgent repairs, ensuring a faster response and resource allocation.