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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 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. 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 much lower cost. 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, a test function and two structural examples are presented and discussed. The second example is also used to show how this methodology can be used to perform reliability-based structural optimization. |
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