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Hao W.,Northwestern Polytechnical University | Lu Z.,Northwestern Polytechnical University | Wei P.,Northwestern Polytechnical University | Feng J.,AVIC Aviation Industry Corporation of China | Wang B.,AVIC Aviation Industry Corporation of China
Structural Safety | Year: 2012

Due to the strong flexibility of artificial neural networks (ANNs), a new method on ANN is presented to analyze the variance based importance measure (VBIM) of correlated input variables. An individual input variable's global variance contribution to the output response can be evaluated and decomposed into the contributions uncorrelated and correlated with other input variables by use of the ANN model. Furthermore, the ANN model is used to decompose the correlated contribution into components, which reflect the contributions of the individual input variable correlated with each of other input variables. Combining the uncorrelated contributions and the correlated contribution components of all input variables, an importance matrix can be obtained to explicitly expose the contribution components of the correlated input variables to the variance of the output response. Several properties of the importance matrix are discussed. One numerical example and three engineering examples are used to verify the presented new method, the results show that the new ANN-based method can evaluate the VBIM with acceptable precision, and it is suitable for the linear and nonlinear output responses. © 2012 .


Wei P.,Northwestern Polytechnical University | Lu Z.,Northwestern Polytechnical University | Hao W.,Northwestern Polytechnical University | Feng J.,AVIC Aviation Industry Corporation of China | Wang B.,AVIC Aviation Industry Corporation of China
Computer Physics Communications | Year: 2012

An important problem in structure reliability analysis is how to reduce the failure probability. In this work, we introduce a main and total effect indices framework of global reliability sensitivity. By decreasing the uncertainty of input variables with high main effect indices, the most reduction of failure probability can be obtained. By decreasing the uncertainty of the input variables with small total effect indices (close to zero), the failure probability will not be reduced significantly. The efficient sampling methods for evaluating the main and total effect indices are presented. For the problem with large failure probability, a single-loop Monte Carlo simulation (MCS) is derived for computing these sensitivity indices. For the problem with small failure probability, the single-loop sampling methods combined with the importance sampling procedure (IS) and the truncated importance sampling procedure (TIS) respectively are derived for improving the calculation efficiency. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the calculation methods and illustrating the engineering significance of the global reliability sensitivity indices. © 2012 Elsevier B.V. All rights reserved.

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