MPC
Project Details

Title:	Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management
Principal Investigators:	Jianli Chen
University:	University of Utah
Status:	Active
Year:	2021
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-668
RiP #:	01785293
RH Display ID:	159606
Keywords:	artificial intelligence, asset management, computer vision, data collection, global positioning system, highway maintenance, mobile applications, smartphones, state departments of transportation

Abstract

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.

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