Abstract
The reliability analysis of complex structural systems requires utilization of approximation methods for calculation of reliability measures with the view of reduction of computational efforts to an acceptable level. The aim is to replace the original limit state function by an approximation, the so-called response surface, whose function values can be computed more easily. In the paper, an artificial neural network based response surface method in the combination with the small-sample simulation technique is introduced. An artificial neural network is used as a surrogate model for approximation of original limit state function. Efficiency is emphasized by utilization of the stratified simulation for the selection of neural network training set elements. The proposed method is employed for reliability assessment of post-tensioned composite bridge. Response surface obtained is independent of the type of distribution or correlations among the basic variables.
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Lehký, D., Šomodíková, M. (2015). Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_4
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DOI: https://doi.org/10.1007/978-3-319-23983-5_4
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