Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Designing robust structures - A nonlinear simulation based approach
Computers and Structures
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A survey on approaches for reliability-based optimization
Structural and Multidisciplinary Optimization
Updating response sensitivity models of nonlinear vibrating structures using particle filters
Computers and Structures
Efficient strategies for reliability-based optimization involving non-linear, dynamical structures
Computers and Structures
Reliability-based design optimization using kriging surrogates and subset simulation
Structural and Multidisciplinary Optimization
Reliability sensitivity analysis for structural systems in interval probability form
Structural and Multidisciplinary Optimization
Reliability sensitivity estimation of linear systems under stochastic excitation
Computers and Structures
Non-parametric stochastic subset optimization for optimal-reliability design problems
Computers and Structures
Global optimization using the asymptotically independent Markov sampling method
Computers and Structures
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Reliability-based design sensitivity analysis involves studying the dependence of the failure probability on design parameters. Conventionally, this requires repeated evaluations of the failure probability for different values of the design parameters, which is a direct but computationally expensive task. An efficient simulation approach is presented to perform reliability-based design sensitivity analysis using only one simulation run. The approach is based on consideration of an 'augmented reliability problem' where the design parameters are artificially considered as uncertain. It is shown that the desired information about reliability sensitivity can be extracted through failure analysis of the augmented problem. The required computational effort is relatively insensitive to the number of uncertain parameters but generally grows exponentially with the number of design parameters whose sensitivity is to be studied. The latter implies that the proposed approach is applicable for studying the sensitivity of a small number of design parameters, say, less than 3, although this drawback appears unavoidable whenever multi-dimensional information is sought. Examples are presented to illustrate applications of the approach to reliability-based retrofit of structures.