Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulating the seismic response of embankments via artificial neural networks
Advances in Engineering Software
Resistance of plate girders under combined bending and shear
EMESEG'10 Proceedings of the 3rd WSEAS international conference on Engineering mechanics, structures, engineering geology
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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.