Better tools to assess natural disaster damage
Credit: UT Arlington
A civil engineer at The University of Texas at Arlington will use unmanned aerial vehicles, or UAVs, to perform reconnaissance after natural disasters and more accurately and quickly assess damage to buildings.
Nick Fang, an assistant professor in the Department of Civil Engineering, is using a $299,000 National Science Foundation grant to accelerate the conventional insurance adjustment process for which homeowners often wait months.
The NSF grant is an early-concept grant for exploratory research. Known as EAGER grants, these projects explore untested but potentially transformative research ideas or approaches. The work is typically considered high risk/high payoff, in the sense that it involves radical approaches, new expertise or novel perspectives.
“We’ll use artificial intelligence and machine learning to improve the UAVs as we deploy them,” Fang said. “We’re waiting for the next big storm. Then we’ll deploy to that location to collect the data and test this innovative approach rigorously.”
If no major storm occurs during the two-year time of the grant, Fang’s team has plenty of data from historical storms, including 2017’s Hurricane Harvey. Fang said some homeowners and property owners had to wait months or years after Harvey before getting insurance adjusters to their sites.
“We are hoping this fixes that wait period,” he said. “Many of these homeowners are stranded. They have to stay with relatives, in hotels or in their cars just to wait for adjusters to come evaluate their catastrophe.”
UTA is collaborating with the University of Hawaii and the University of Wisconsin on the project, which shows not only the importance of UTA’s research but also the immediacy of it, said Ali Abolmaali, chair of civil engineering.
“Dr. Fang’s work has so much relevance, given the many natural disasters that have happened in the last several years,” Abolmaali said. “This project speaks to UTA’s strategic plan themes of sustainable urban communities, global environmental impact and data-driven discovery.”