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. But what about civil engineering and city planning? 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.”