AI-enabled damage estimation of hurricane-impacted residential communities through CFD simulations and stratified sampling

This study introduces a novel framework for estimating hurricane-induced damage to individual buildings by integrating AI-based 3D building modeling, Computational Fluid Dynamics (CFD), and stratified sampling. By leveraging the precise geometric characteristics of individual buildings extracted fro...

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Vydáno v:Journal of wind engineering and industrial aerodynamics Ročník 251; s. 105806
Hlavní autoři: Kim, Sejin, Ding, Fei, Spence, Seymour M.J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2024
Témata:
AI-Based 3D modeling
Computational fluid dynamics
Enhanced stratified sampling
Hurricane damage estimation
Vulnerability modeling
Enhanced stratified sampling
Computational fluid dynamics
Hurricane damage estimation
AI-Based 3D modeling
Vulnerability modeling
ISSN:0167-6105, 1872-8197
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Shrnutí:This study introduces a novel framework for estimating hurricane-induced damage to individual buildings by integrating AI-based 3D building modeling, Computational Fluid Dynamics (CFD), and stratified sampling. By leveraging the precise geometric characteristics of individual buildings extracted from digital data, the framework enables the accurate computation of pressure distributions for individual buildings through high-fidelity CFD simulations. The subsequent integration of an AI-based building component detection technique with vulnerability modeling and enhanced stratified sampling enables the rapid computation of the component and building-level failure probabilities. Applied to a case study in Atlantic City, NJ, the research underscores the effectiveness of realistic geometric characterizations and the inclusion of neighboring buildings in estimating individual-building level damage. Unlike conventional archetype-based approaches, the proposed methodology offers individual-building level risk assessment, reflecting the crucial role played by geometric and aerodynamic variables. The framework offers a promising step towards automated high-fidelity assessments of hurricane risks, leveraging detailed modeling and simulation to contribute to more resilient communities. •A computational framework for building-specific wind vulnerability modeling is presented.•AI is used to characterize the vulnerable components of each building of a community.•CFD in the form of LES is used to characterize the dynamic pressures on each building.•Stratified sampling is used to propagate uncertainty and estimate damage at multiple scales.•The modeling framework is demonstrated on a cluster of buildings subject to hurricane winds.
ISSN:0167-6105
1872-8197
DOI:10.1016/j.jweia.2024.105806