Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management

Road asset management aims at optimizing the allocation of road maintenance resources considering asset conditions and the associated costs. Understanding the current asset conditions is crucial as the first step of efficient asset management practice. Currently, state DOTs mostly rely on the LIDAR inspection for data collection with high operational cost, which can only be completed once per a couple of years. The lack of timely data would inevitably create barriers in daily maintenance works. Hence, there is an urgent need of developing an efficient data collection technology that can gather the required information on a more frequent basis. To tackle this critical issue, this research aims to introduce an efficient, convenient, and affordable approach to collect maintenance asset data on a much more frequent basis. The proposed technology will use a smartphone app to record videos and GPS locations, which can be easily attached to UDOT fleet vehicles for data collection. Then, by leveraging computer vision techniques, this research aims to develop the artificial intelligence packages for extracting and analyzing road asset information automatically from recorded videos.


  • English


  • Status: Active
  • Funding: $90495
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108
  • Project Managers:

    Tolliver, Denver

  • Performing Organizations:

    Dept. of Civil and Environmental Engineering

    University of Utah
    Salt Lake City, UT  United States 
  • Principal Investigators:

    Chen, Jianli

  • Start Date: 20210924
  • Expected Completion Date: 20240731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Source Data: MPC-668

Subject/Index Terms

Filing Info

  • Accession Number: 01785293
  • Record Type: Research project
  • Source Agency: Mountain-Plains Consortium
  • Contract Numbers: 69A3551747108
  • Files: UTC, RIP
  • Created Date: Oct 22 2021 10:18AM