Infrastructure protectionHitting the road: Redefining infrastructure maintenance
The results of the Remote-Sensing and GIS-enabled Asset Management System, Phase 2 (RS-GAMS2) project, a 2-year, $1.9 million research enterprise, were released in late August. The project promises to revolutionize the way U.S. roads are inventoried, managed, and maintained. Sponsored by grants from the U.S. and Georgia Departments of Transportation, a multi-disciplinary team of researchers has been developing the system for the past two years, using 18,000 miles of Georgia roadways as their laboratory.
Transportation officials and researchers from several states gathered at Georgia Tech on 29 August to review the results of the Remote-Sensing and GIS-enabled Asset Management System, Phase 2 (RS-GAMS2) project, a 2-year, $1.9 million research enterprise that promises to revolutionize the way U.S. roads are inventoried, managed, and maintained.
A Georgia Tech release notes that to the delight of its principal investigator, School of Civil and Environmental Engineering’s (CEE) Dr. James Tsai, this research will also change the way civil engineers do their work.
“Fifty years ago, civil engineers built roads and were done, really. But now the challenge is different. We must maintain those roads in a cost-effective, sustainable way, while not interrupting the flow of traffic,” said Tsai, who partnered with Georgia Tech colleagues Dr. Tony Yezzi (School of Electrical and Computer Engineering [ECE]) and Dr. Zhaohua Wang (College of Architecture [CoA]) on this project.
“The RS-GAMS allows us to easily look at our roads and decide when, where, and what maintenance and rehabilitation methods should be used. It will give us the greatest return on our investment.”
The release reports that the Remote Sensing and GIS-enabled Asset Management System (RS-GAMS) coordinates emerging technologies into a seamless process that more accurately and more cheaply assesses pavement, bridge, and roadway assets. The system employs light detection and ranging (LiDAR), 3D lasers, imaging, inertia detection, and GPS/GIS technologies to collect and analyze data on everything from missing road signs to cracked pavement. It also uses multi-sensor data fusion, image/signal processing, and artificial intelligence algorithms to deliver a complete picture of roadway conditions to maintenance and planning officials.
Sponsored by grants from the U.S. and Georgia Departments of Transportation, Tsai’s multi-disciplinary research team has been developing the system for the past two years, using 18,000 miles of Georgia roadways as their laboratory.
The August release of their findings couldn’t have come at a better time, according to J. B. Butch Wlaschin, the director of the Office of Asset Management for the Federal Highway Administration (FHWA).
“A new federal law is requiring the states to have comprehensive, data-driven asset management plans in place to show that their pavement and bridges are in a state of good repair. But there are no protocols, no standards, no methodologies for assessing the condition of our roadways from state to state,” says Wlaschin.
