Solving Inverse Structural Reliability Problem Using Artificial Neural Networks and Small-Sample Simulation

Abstract

A new general inverse reliability analysis approach based on artificial neural networks is proposed. An inverse reliability analysis is a problem of obtaining design parameters corresponding to a specified reliability (reliability index or theoretical failure probability). Design parameters can be deterministic or they can be associated with random variables. The aim is to generally solve not only single design parameter cases but also multiple parameter problems with given multiple reliability constraints. Inverse analysis is based on the coupling of a stochastic simulation of the Monte Carlo type (the small-sample simulation method Latin hypercube sampling) and an artificial neural network. The validity and efficiency of this approach is shown using numerical examples with single as well as multiple reliability constraints and with single as well as multiple design parameters, and with independent basic random variables as well as random variables with prescribed statistical correlations.

Inverse Response Surface Method in Reliability-Based Design

“…A soft computing-based inverse reliability method has been proposed by Lehký and Novák [8]. The method is based on the coupling of a stratified Latin hypercube sampling (LHS) simulation technique and an artificial neural network (ANN).…”

“…Once the ANN has been trained, it represents an approximation consequently utilized in a following way: To provide the best possible set of design parameters corresponding to prescribed reliability. See [8] for more complex explanation of the method.…”

“…Therefore, an inverse response surface method (IRSM) is proposed. It is based on a coupling of the adaptive RSM of Bucher and Bourgund [7] and the ANN-based inverse reliability method of Lehký and Novák [8]. The method is inspired by the procedure of Li [9], which combined the RSM with the Newton-Raphson iterative algorithm to solve inverse reliability problem [10].…”

Pre-posterior optimization of sequence of measurement and intervention actions under structural reliability constraint

“…Der Kiureghian et al [21] were among the firsts to study inverse reliability problems, where parameter values satisfying a reliability constraint are sought. More recently, Lehkỳ and Novák [22] also approach this problem using a method based on Artificial neural network.…”

Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

“…The first one utilizes ANN too, but in a different way: Computational time is reduced by using a small-sample simulation technique called Latin hypercube sampling (LHS) in ANN based inverse problem proposed by Novák and Lehký in [10] and [11] first.…”