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Published August 2003 | public
Journal Article

Subset Simulation and its Application to Seismic Risk Based on Dynamic Analysis

Abstract

A method is presented for efficiently computing small failure probabilities encountered in seismic risk problems involving dynamic analysis. It is based on a procedure recently developed by the writers called Subset Simulation in which the central idea is that a small failure probability can be expressed as a product of larger conditional failure probabilities, thereby turning the problem of simulating a rare failure event into several problems that involve the conditional simulation of more frequent events. Markov chain Monte Carlo simulation is used to efficiently generate the conditional samples, which is otherwise a nontrivial task. The original version of Subset Simulation is improved by allowing greater flexibility for incorporating prior information about the reliability problem so as to increase the efficiency of the method. The method is an effective simulation procedure for seismic performance assessment of structures in the context of modern performance-based design. This application is illustrated by considering the failure of linear and nonlinear hysteretic structures subjected to uncertain earthquake ground motions. Failure analysis is also carried out using the Markov chain samples generated during Subset Simulation to yield information about the probable scenarios that may occur when the structure fails.

Additional Information

©ASCE. The manuscript for this paper was submitted for review and possible publication on January 7, 2002; approved on November 21, 2002. This paper was based upon work partly supported by the Pacific Earthquake Engineering Research Center under the National Science Foundation Cooperative Agreement No. CMS-9701568. This support is gratefully acknowledged. Part of this work was prepared when the first writer was at graduate standing at the California Institute of Technology, whose generous financial support is gratefully acknowledged.

Additional details

Created:
August 22, 2023
Modified:
October 18, 2023