A new directional simulation method for system reliability. Part II: application of neural networks

Abstract

A challenge in directional importance sampling is in identifying the location and the shape of the importance sampling density function when a realistic limit state for a structural system is considered in a finite element-supported reliability analysis. Deterministic point refinement schemes, previously studied in place of directional importance sampling, can be improved by prior knowledge of the limit state. This paper introduces two types of neural networks that identify the location and shape of the limit state quickly and thus facilitate directional simulation-based reliability assessment using the deterministic Fekete point sets introduced in the companion paper. A set of limit states composed of linear functions are used to test the efficiency and possible directional preference of the networks. These networks are shown in the tests and examples to reduce the simulation effort in finite element-based reliability assessment.