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Reliability-based structural optimization using neural networks and Monte Carlo simulation. (English) Zbl 1101.74377
Summary: 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:
74S30Other numerical methods in solid mechanics
74P10Optimization of other properties (solid mechanics)
92B20General theory of neural networks (mathematical biology)
62N05Reliability and life testing (survival analysis)
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References:
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