Session Abstracts:
Senior Engineer
Applied Research Associate
Abstract: Coming Soon
Associate Professor
UCLA
Benchmarking the performance of alternative strategies for gaining computational efficiency in regional seismic risk and resilience assessments
Co-Authors: Laxman Dahal (Arup) and Kuanshi Zhong (University of Cincinnati)
Abstract: Regional-scale seismic performance assessments are useful to a range of application contexts. Undoubtedly, the various use cases may lead to different computational demand requirements. For instance, evaluating alternative recovery-based design solutions requires a building-specific approach because of the need to understand how changes in specific seismic design parameters affect performance. On the other hand, if the goal is to identify vulnerable neighborhoods to inform emergency preparedness programs, the building-specific approach might not be necessary. As such, a one-size-fits-all regional seismic risk and resilience quantification solution is not advisable.
This study comparatively evaluates the performance of four strategies for gaining computational efficiency in regional risk and resilience assessment. The approaches vary based on the level of fidelity and resolution used in the simulation. Fidelity is used to describe the level of detail used to simulate the structural response and performance of a given asset (e.g., 3D MDOF versus SDOF structural model). Resolution refers to the extent to which spatially distributed hazard and assets are aggregated within the simulation environment. An inventory of over 15,000 single and multi-family woodframe residences in the City of Los Angeles is used as the testbed. Regional seismic performance is quantified using the probabilistic distribution of economic losses and functional recovery times. The four approaches are benchmarked against the results from a “high-fidelity”, “high-resolution” simulation-based assessment that uses 3D nonlinear structural modeling, component-level damage evaluation and site-specific hazard and building performance characterization. Both scenario- and stochastic event set-based regional assessments are considered in the benchmark performance evaluations.