KDOT project wins "Sweet Sixteen" award from AASHTO

KU Professors Dr.Robert Parsons, State Transportation Engineer Burt Morey and Dr. Jie Han who coordinated graduate student Mustapha Rahmaninezhad's research on the Software for Load Distrubution on Low-Fill Box Culverts project, which earned KDOT a 2019 Sweet 16 Research award.

By Mallory Aye, KDOT Headquarters

Project presented award: A KDOT project was presented with a “Sweet Sixteen” High Value Research award at the 2019 National Research Advisory Committee and TRB State Representatives Meeting, which took place July 22–25 in Santa Fe, N.M.

The research project K-TRAN: KU-16-5, Software for Load Distribution on Low-Fill Box Culverts: User’s Manual, was conducted by University of Kansas Professors Jie Han and Robert Parsons with graduate students Seyed Mustapha Rahmaninezhad and Fei Wang in the Department of Civil, Environmental and Architectural Engineering.

Structures and Geotechnical Services Chief Calvin Reed recognizes the Bridge Evaluation team on Nov. 4 at the Materials and Research Lab for their role in the K-TRAN research project and the related software that they routinely use. 

The project team was successful in developing a stress distribution program to better test the various forms of stress that may affect shallowly buried culverts. The program was designed to compute the Equivalent Live Load Distribution Factor (ELLDF) for distributed loads, distributed area and distributed stress on buried box structures. 

This data is compatible to be input into current AASHTO design software to better determine how pavement affects load distribution onto buried box culverts.

The “Sweet Sixteen” award exists as part of an initiative by the AASHTO Research Advisory Committee to identify and document recently completed “High Value Research” projects. Every year, states are asked to submit research projects of recognizable qualitative or quantitative benefit for consideration. The Sweet Sixteen award recipients are selected from the qualifying high value projects, with four projects being chosen from each of the four AASHTO regions.