Data‐driven machine learning for multi‐hazard fragility surfaces in seismic resilience analysis
Mojtaba Harati, John W. van de Lindt
Abstract
Offshore earthquakes and subsequent tsunamis pose significant risks to many coastal populations worldwide. This paper introduces a data-driven machine learning model that synthesizes accurate 3D earthquake–tsunami fragility surfaces from randomly selected 2D fragility curves. The integration of physics-based simulations enhances the model's reliability for these specific hazards, making it a valuable tool for multi-hazard analysis in earthquake–tsunami contexts. Additionally, by shifting 2D fragility curves to represent retrofitted structural systems, the model can generate earthquake–tsunami fragility surfaces for community-level mitigation studies. While the model is demonstrated for earthquake–tsunami scenarios, its methodology architecture has the potential to contribute to other multi-hazard situations for the initial conditions in multi-hazard community resilience analysis.