Reliability-based structural optimization using neural networks and Monte Carlo simulation.

*(English)*Zbl 1101.74377Summary: This paper examines the application of neural networks (NN) to reliability-based structural optimization of large-scale structural systems. The failure of the structural system is associated with the plastic collapse. The optimization part is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method incorporating the importance sampling technique for the reduction of the sample size. In this study two methodologies are examined. In the first one an NN is trained to perform both the deterministic and probabilistic constraints check. In the second one only the elasto-plastic analysis phase, required by the MCS, is replaced by a neural network prediction of the structural behaviour up to collapse. The use of NN is motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required by MCS.

##### MSC:

74S30 | Other numerical methods in solid mechanics (MSC2010) |

74P10 | Optimization of other properties in solid mechanics |

92B20 | Neural networks for/in biological studies, artificial life and related topics |

62N05 | Reliability and life testing |

##### Keywords:

structural optimization; reliability analysis; Monte Carlo simulation; evolution strategies; neural networks; parallel computations
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\textit{M. Papadrakakis} and \textit{N. D. Lagaros}, Comput. Methods Appl. Mech. Eng. 191, No. 32, 3491--3507 (2002; Zbl 1101.74377)

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