RELIABILITY ASSESSMENT OF STRUCTURES BY MONTE CARLO SIMULATION AND NEURAL NETWORKS

This paper examines a methodology for computing the probability of structural failure by combining Monte Carlo Simulation (MCS) and Artificial Neural Networks (ANN). MCS is a 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 implies a great number of structural analyses which can become excessively time consuming. In the present study, nonlinear structural analysis is involved and therefore the computational effort of MCS will be resonated comparing with that of the linear analysis. The proposed methodology makes use of capability of a ANN to approximate a function for reproducing structural behavior, allowing the computation of performance measures at a much lower cost. In order to assess the validity of this methodology, a structural example is presented and discussed. The numerical results demonstrate the efficiency of the proposed methodology for the structural reliability analysis.