Over the past several decades, artificial intelligence has advanced tremendously, and today it promises new opportunities for more accurate healthcare, enhanced national security and more effective education, researchers say. How do increased computing power and machine learning help create safer, more sustainable and resilient infrastructure? U.S. National Science Foundation-funded researchers at the Computational Modeling and Simulation Center, or SimCenter, have developed a suite of tools called BRAILS -- short for Building Recognition using AI at Large-Scale -- that can automatically identify characteristics of buildings in a city and detect the risks a city's structures would face in the event of an earthquake, hurricane or tsunami. SimCenter is part of the NSF-funded Natural Hazards Engineering Research Infrastructure program and serves as a computational modeling and simulation center for natural hazards engineering researchers at the University of California, Berkeley. Charles Wang, the lead developer of BRAILS, says the project grew out of a need to "quickly and reliably characterize the structures in a city. We want to simulate the impact of hazards on all the buildings in a region, but we don't have a description of the building attributes."

AI is providing new opportunities in a range of fields, from business to industrial design to entertainment. 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, 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. Charles (Chaofeng) Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS, says the project grew out of a need to quickly and reliably characterize the structures in a city. "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," Wang said. "For example, in the San Francisco Bay area, there are millions of buildings.

Artificial intelligence is providing new opportunities in a range of fields, from business to industrial design to entertainment. 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. Charles (Chaofeng) Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS, says the project grew out of a need to quickly and reliably characterize the structures in a city. "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," Wang said. "For example, in the San Francisco Bay area, there are millions of buildings.