Efficient system reliability analysis of earth slopes based on support vector machine regression model

Metya, Subhadeep, Mukhopadhyay, Tanmoy, Adhikari, Sondipon and Bhattacharya, Gautam (2017) Efficient system reliability analysis of earth slopes based on support vector machine regression model. In, Handbook of Neural Computation. Elsevier Inc., pp. 127-143. (doi:10.1016/B978-0-12-811318-9.00007-7).

Abstract

This chapter presents a surrogate-based approach for system reliability analysis of earth slopes considering random soil properties under the framework of limit equilibrium method of slices. The support vector machine regression (SVR) model is employed as a surrogate to approximate the limit-state function based on the Bishop's simplified method coupled with a nonlinear programming technique of optimization. The value of the minimum factor of safety and the location of the critical slip surface are treated as the output quantities of interest. Finally, Monte Carlo simulation in combination with Latin hypercube sampling is performed via the SVR model to estimate the system failure probability of slopes. Based on the detailed results, the performance of the SVR-based proposed procedure seems very promising in terms of accuracy and efficiency.

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Contributors

Author: Tanmoy Mukhopadhyay

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