AI Could Set a New Bar for Designing Hurricane-Resistant Buildings

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Being able to withstand hurricane-force winds is the key to a long life for many buildings on the Eastern Seaboard and Gulf Coast of the U.S. Determining the right level of winds to design for is tricky business, but support from artificial intelligence may offer a simple solution.

Being able to withstand hurricane-force winds is the key to a long life for many buildings on the Eastern Seaboard and Gulf Coast of the U.S. Determining the right level of winds to design for is tricky business, but support from artificial intelligence may offer a simple solution.

Equipped with 100 years of hurricane data and modern AI techniques, researchers at the National Institute of Standards and Technology (NIST) have devised a new method of digitally simulating hurricanes. The results of a study published today in Artificial Intelligence for the Earth Systems demonstrate that the simulations can accurately represent the trajectory and wind speeds of a collection of actual storms. The authors suggest that simulating numerous realistic hurricanes with the new approach can help to develop improved guidelines for the design of buildings in hurricane-prone regions.

State and local laws that regulate building design and construction — more commonly known as building codes — point designers to standardized maps. On these maps, engineers can find the level of wind their structure must handle based on its location and its relative importance (i.e., the bar is higher for a hospital than for a self-storage facility). The wind speeds in the maps are derived from scores of hypothetical hurricanes simulated by computer models, which are themselves based on real-life hurricane records.

“Imagine you had a second Earth, or a thousand Earths, where you could observe hurricanes for 100 years and see where they hit on the coast, how intense they are. Those simulated storms, if they behave like real hurricanes, can be used to create the data in the maps almost directly,” said NIST mathematical statistician Adam Pintar, a study co-author.

Read more at National Institute of Standards and Technology (NIST)

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