%0 Journal Article
%T Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine
%A Shao-Fei Jiang
%A Da-Bao Fu
%A Si-Yao Wu
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/586191
%X To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed. 1. Introduction In recent years, a number of structural reliability assessment methods, including first-order reliability method (FORM) [1], response surface method (RSM) [2], and Monte-Carlo simulation method (MCSM) [3], have been developed and applied to practical engineering structures. Among these methods, the FORM is usually used to directly estimate structural failure probability in the case of the explicit limit state functions. In contrast, the RSM and MCSM are widely used in the case that the limit state functions are complex and implicit. The main idea of RSM is to transfer the original implicit limit state function to an approximated explicit expression, which will then be used for the assessment of failure probability with the aid of FORM. However, in most cases, the hypothetical explicit expressions can hardly be found to represent precisely the original nonlinear and complex functions; thus RSM usually causes an unallowable error, even a wrong assessment result. MCSM not only is the most precise method for failure probability assessment, but also solves theoretically all of reliability problems. However, MCSM is a time-consuming process. It is suitable for solving such problem when structural failure probability is small, because a number of samples are required for the purpose of obtaining a reasonable result. To overcome the low-fidelity of RSM and low computing efficiency of MCSM, several researchers have attempted to construct the limit state function based on the intellectual techniques, such as artificial neural networks [4, 5] and SVM [6, 7]. Due to the
%U http://www.hindawi.com/journals/mpe/2014/586191/