UTSA professors get funding to work with artificial intelligence to improve disaster recovery | UTSA Today | UTSA

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Do, who teaches civil engineering in the Margie and Bill Cleese College of Integrated Engineering and Design, said their research could help speed up the decision-making process related to repairing transportation infrastructure — making it easier to bring in relief supplies and search and rescue and reconstruction teams. Kits.

“Making decisions to restore regional transportation networks in a post-earthquake environment is very complex due to many uncertainties and resource constraints throughout the restoration time horizon,” Du said. “But recent advances in artificial intelligence allow for adaptive decision-making to help restoration teams prioritize which roads, bridges or railway lines need repair first.”

Jiannan Cai

“This research will advance risk-aware and explainable AI techniques, such that more transparent and risk-aware interpretations of AI-derived policies will be provided for trustworthy post-earthquake recovery planning,” said Cai, who teaches civil and environmental engineering and environmental engineering. Construction management.

Final restoration decisions are always left up to stakeholders, Du said. AI exists to provide “quantitative, risk-based measurements of the long-term benefits of options and then suggest optimal actions,” he said. “Such information can be very useful, especially in a post-disaster situation where stakeholders need to make quick and strategic decisions under enormous pressure.”

Du and Cai chose to focus on transportation networks due to the highly uncertain nature of earthquakes and their serious impact on the physical infrastructure of the region. They will combine AI models with civil engineering domain knowledge to ensure they reflect the characteristics of the transportation infrastructure network to make reliable decisions.

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