A Texas A&M construction science professor is working to lower the cost of highway construction bids by optimizing data-driven construction scheduling methods in advance of the U.S. government implementing the $1.5 trillion highway infrastructure rebuilding plan proposed last February by the Trump administration.
Kunhee Choi, associate professor of construction science is joining colleagues from Iowa State University and Northeastern University on this 20-month, $500,000 project, “Systematic Approach for Estimating Construction Contract Time,” funded by the Transportation Research Board of the National Cooperative Highway Research Program.
The research team will develop a comprehensive guidebook detailing procedures, methods and tools for estimating contract time for highway projects.
“In current practice, contract times are estimated largely on historical data, intuition and engineers’ gut feelings,” Choi said. “The guidebook will assist state transportation agencies in developing the most reliable contract times for highway renewal projects.”
When the Department of Transportation sets a project period significantly shorter or longer than industry estimates, competing contractors are forced to increase bid cost to either accelerate or delay project construction schedules.
“Either way, the public loses due to inaccurate and sometimes arbitrary contract completion times,” Choi said.
Choi’s team will use information from DOT databases to develop a data-driven, risk-based methodology that credibly estimates contract time for most projects, including those using alternative contracting methods.
Choi is the holder of the Cecil Windsor Endowed Professorship in Construction Science. He earned a bachelor of architectural engineering from Korea University, a master of science in construction management from Texas A&M and a Ph.D. in Civil and Environmental Engineering from the University of California at Berkeley.
A member of the Texas A&M faculty since 2010, Choi’s research has centered around experimenting with and creating state-of-the-art methods and analytical models to optimize the efficiency of the U.S. transportation system by working to improve public safety and mobility.