Abstract
Selecting and using an appropriate structural reliability method is critical for the success of structural reliability analysis and reliability-based design optimization. However, most of existing structural reliability methods are developed and designed for a single limit state function and few methods can be used to simultaneously handle multiple limit state functions in a structural system when the failure probability of each limit state function is of interest, for example, in a reliability-based design optimization loop. This article presents a new method for structural reliability analysis with multiple limit state functions using support vector machine technique. A sole support vector machine surrogate model for all limit state functions is constructed by a multi-input multi-output support vector machine algorithm. Furthermore, this multi-input multi-output support vector machine surrogate model for all limit state functions is only trained from one data set with one calculation process, instead of constructing a series of standard support vector machine models which has one output only. Combining the multi-input multi-output support vector machine surrogate model with direct Monte Carlo simulation, the failure probability of the structural system as well as the failure probability of each limit state function corresponding to a failure mode in the structural system can be estimated. Two examples are used to demonstrate the accuracy and efficiency of the presented method.
Highlights
During the past few decades, structural reliability methods have been gained increasing interest for rational treatment of the uncertainties in engineering structures
It is well known that most of traditional structural reliability methods cannot be applied to deal with multiple limit state functions (LSFs) simultaneously, when the failure probability of each LSF is of interest
A new structural reliability method using MIMO-support vector machine (SVM) is presented to handle multiple LSFs for this issue which may arise in an reliability-based design optimization (RBDO) problem and/or a problem with multiple failure modes
Summary
During the past few decades, structural reliability methods have been gained increasing interest for rational treatment of the uncertainties in engineering structures. This is the first attempt to employ MTLS-SVM for structural reliability analysis of multiple LSFs. A new random sampling method, combining the Latin hypercube sampling (LHS) and uniform sampling (US), is developed to generate the training data (supporting points) set with a good coverage of the input random space. It cannot be used for dealing with a complex system with multiple outputs, for example, the demand of dealing with multiple LSFs in an RBDO problem as in this study To overcome this issue, various MIMO-SVM techniques have been proposed to meet this demand.[35,36,37,38] In this article, a MTLS-SVM35,36 is employed to build up a single surrogate model which can approximate multiple LSFs. Considering a system has m outputs, the training sample size is l. A larger value of k needs to be set
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