Structural reliability analysis using Monte Carlo simulation and neural networks

This paper examines a methodology for computing the probability of structural failure by combining neural networks (NN) and Monte Carlo simulation (MCS). MCS is a very powerful tool, simple to implement and capable of solving a broad range of reliability problems. However, its use for evaluation of very low probabilities of failure, of the order of magnitude currently found in structural reliability, implies a great number of structural analyses, which can become excessively time consuming. The proposed methodology makes use of the capability of a NN to approximate a function for reproducing structural behavior, allowing the computation of performance measures at a fraction of the cost of the corresponding structural analysis. This approach seems very attractive, and its main challenge lies in the ability of a NN to approximate accurately complex structural response. In order to assess the validity of this methodology, examples are presented and discussed.