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Traditional damage models fail without historical earthquake loss data, especially for new structural systems. In cities that have not seen a major earthquake for decades, it is difficult to confirm whether buildings are truly safe.
This webinar introduces a physics-based framework that scales nonlinear time history analysis from a single building to hundreds of thousands across a city. It leverages advanced technologies to deliver rapid city scale seismic assessment within hours after an earthquake.
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Description
Conventional damage assessment models rely on historical earthquake loss data, making them unreliable in regions with limited seismic records or for new structural systems. Because these approaches are based on data from high seismic areas, they often apply assumptions that do not reflect local conditions, creating uncertainty in evaluating actual building safety.
This webinar introduces a physics based simulation approach that computes structural performance from fundamental behavior, removing reliance on incomplete data. Validated through real world cases including a large scale analysis of downtown San Francisco, the framework integrates GPU computing, CIM (GIS + BIM), and AI to enable city scale analysis and deliver damage assessments within two hours after an earthquake.
Key Points
Understand why city-scale nonlinear time history analysis (NLTHA) is essential and how GPU-based physics simulations enable accurate, large-scale modeling beyond empirical limitations.
Learn how to implement a CIM-based workflow that integrates GIS and BIM to run multi-hazard scenarios (earthquake, fire, wind) within a unified data structure.
Discover how AI-driven rapid damage assessment (e.g., RED-ACT) delivers city-scale impact predictions within 2 hours, achieving up to 1,500× faster results than traditional NLTHA methods.
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