An Artificial Neural Network Approach to Solve Inverse Reliability Problems

摘要

An artificial neural network approach to solve inverse reliability problems is proposed. An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability measures (reliability index or theoretical failure probability. Design parameters can be deterministic or they can be associated to random variables described by statistical moments. The aim is to solve generally not only the single design parameter case but also the multiple parameter problems with given multiple reliability constraints. A new general approach of inverse reliability analysis is proposed. The inverse analysis is based on the coupling of a stochastic simulation of Monte Carlo type and an artificial neural network. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling used for the stochastic preparation of the training set. That is needed for proper adjustment of synaptic weights and biases of specified neural network. The validity and efficiency of the approach is shown using numerical examples with single as well as with multiple reliability constraints and with single as well as with multiple design parameters. Together with identification of design parameters in cases with independent basic random variables, the case with prescribed correlation among some of the parameters was treated.