Sensitivity analysis for seismic risk through stochastic ground motion modeling




Probabilistic Sensitivity Analysis for Seismic Risk Based on Stochastic Sampling and Focusing on Stochastic Ground Motion Modeling



Seismic risk assessment requires description of the time-history for future earthquakes. Stochastic ground motion models, either “source-based” or “record-based”, are emerging as an attractive approach for such a task.  Description of the uncertainty in the regional seismicity and in the predictive relationships connecting seismological characteristics to ground motion properties (such as strong motion duration or frequency content) facilitates then a complete probabilistic characterization for the seismic hazard and an augmented framework for seismic risk description. Probabilistic sensitivity analysis identifies which of the uncertain model parameters, i.e. risk factors, have higher contribution to the overall seismic risk



The objective of the project is to develop a novel methodology to quantify the importance of the various excitation-related risk factors on the structural system performance and the design ground motion selection. To accomplish this goal, a probabilistic, simulation-based, modeling framework are being established for describing seismic risk and computationally efficient approaches are developed, based on stochastic sampling, for quantifying the influence of the uncertain model characteristics on seismic risk. The project involves further advancement of these tools and applications to a variety of structural systems with diverse performance definitions and with different ground motion models for characterizing the earthquake time-history. This will ultimately provide a methodology for efficient probabilistic sensitivity analysis and for quantifying the importance of various risk factors in ground motion selection and in seismic structural design practice.





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This material is based in part upon work supported by the National Science Foundation under NSF grant number CMMI 1030726. This support is greatly appreciated. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.