Optimum Scheme Selection for Multilayer Perceptron-Based Monte Carlo Simulation of Slope System Reliability
Publication: International Journal of Geomechanics
Volume 21, Issue 10
Abstract
Surrogate models are helpful tools to enhance the efficiency for intensive computations of the factor of safety (FoS) in probabilistic slope stability evaluation. This study presents a multilayer perceptron (MLP)-based surrogate model combined with the Monte Carlo simulation (MCS) for system reliability analysis of earth slopes. The MLP-based surrogate model is constructed derived from the space-filling Latin hypercube sampling (LHS) for a global prediction of the FoS. Several factors affecting the performance of the MLP model are studied in detail, including the training algorithm, the generation method and size of samples, and the hyperparameters. Three examples with system effects are tested to verify the performance of the proposed method. The results show that the MLP-based MCS can achieve high accuracy and efficiency for the system failure probability assessment of soil slopes in different failure zones.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China [NSFC Grant Nos. 51879091, 52079045, and 41772287]. The last author would also like to thank financial support from the Key R&D Project of Zhejiang Province (2021C03159).
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Published In
International Journal of Geomechanics
Volume 21 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 16, 2021
Accepted: May 29, 2021
Published online: Jul 29, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 29, 2021
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