Estimation of Structural Collapse Reliability via Response Surface Method and a Hybrid of Neuro-Fuzzy Networks with Meta-heuristic Algorithms

Authors

1 Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2 School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Earthquakes are catastrophic natural phenomena that occasionally lead to the collapse of structures. Considering the importance of collapse impacts, the present study primarily focuses on estimating the reliability of collapse probability for complicated structures for which no explicit limit state functions exist. Different simulation methods are used for combining uncertainties, including the Monte Carlo, Latin Hypercube Sampling, and the importance of sampling approaches. Simulation methods require several samples to cover the probabilistic distribution of uncertainties. To deal with this problem, the response surface method (RSM) and artificial neural networks (ANNs) integrated with the simulation method have been proposed for reducing the computation effort (Beheshti-Aval et al., 2015; Khojastehfar et al., 2015). Common methods applied to evaluate reliability include 1) reliability-based methods, involving the first-order reliability method (FORM) and second-order reliability method (SORM) and 2) Monte Carlo simulation methods (Nowak and Collins, 2000).

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Main Subjects

References

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Journal of Civil and Environmental Engineering
Volume 52.2, Issue 107 - Serial Number 107
Summer 2022
October 2022
Pages 27-45

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