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IMAGE: Visualization of the percentage of a building s repair cost to its replacement value after a magnitude 7.0 earthquake in San Francisco. view more
Credit: Chaofeng Wang, SimCenter, UC Berkeley
Artificial intelligence is providing new opportunities in a range of fields, from business to industrial design to entertainment. But how about civil engineering and city planning? How might machine- and deep-learning help us create safer, more sustainable, and resilient built environments?
A team of researchers from the NSF NHERI SimCenter, a computational modeling and simulation center for the natural hazards engineering community based at the University of California, Berkeley, have developed a suite of tools called BRAILS Building Recognition using AI at Large-Scale that can automatically identify characteristics of buildings in a city and even detect the risks that a city s structures would face in an earthquake, hurricane, or tsunami.
May 19, 2021
Scientists are using data from Google Maps and satellite images to power artificial intelligence applications that can automatically identify characteristics of city’s buildings that would be vulnerable in an earthquake, hurricane or tsunami.
Researchers from the National Science Foundation’s SimCenter, an engineering community focused on modeling the impact of natural hazards, built the Building Recognition using AI at Large-Scale suite of tools. BRAILS creates enhanced building databases for cities by running artificial intelligence-powered simulations on high-performance computers at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. We want to simulate the impact of hazards on all of the buildings in a region, but we don t have a description of the building attributes, said Charles Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS told TACC News. Using AI, we are able to