Article2009

Reliability sensitivity analysis with random and interval variables

171 citations0/0 files storedInternational Journal for Numerical Methods in Engineering

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Geotechnical Engineering and Analysis

Abstract

Abstract In reliability analysis and reliability‐based design, sensitivity analysis identifies the relationship between the change in reliability and the change in the characteristics of uncertain variables. Sensitivity analysis is also used to identify the most significant uncertain variables that have the highest contributions to reliability. Most of the current sensitivity analysis methods are applicable for only random variables. In many engineering applications, however, some of uncertain variables are intervals. In this work, a sensitivity analysis method is proposed for the mixture of random and interval variables. Six sensitivity indices are defined for the sensitivity of the average reliability and reliability bounds with respect to the averages and widths of intervals, as well as with respect to the distribution parameters of random variables. The equations of these sensitivity indices are derived based on the first‐order reliability method (FORM). The proposed reliability sensitivity analysis is a byproduct of FORM without any extra function calls after reliability is found. Once FORM is performed, the sensitivity information is obtained automatically. Two examples are used for demonstration. Copyright © 2009 John Wiley & Sons, Ltd.

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References

2017Structural Reliability Analysis and Prediction 1974Exact and Invariant Second-Moment Code Format 2001Probability, Reliability and Statistical Methods in Engineering Design 1994Computational methods for efficient structural reliability and reliability sensitivity analysis 2004An exploration of alternative approaches to the representation of uncertainty in model predictions 2003Sensitivity & Uncertainty Analysis, Volume 1 2004Uncertainty quantification using evidence theory in multidisciplinary design optimization 2004An exploration of alternative approaches to the representation of uncertainty in model predictions

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