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Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
Subhadeep Metya,
Tanmoy Mukhopadhyay,
Sondipon Adhikari,
Gautam Bhattacharya
Handbook of Neural Computation, Pages: 127 - 143
Swansea University Author: Sondipon Adhikari
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DOI (Published version): 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 f...
| Published in: | Handbook of Neural Computation |
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| ISBN: | 978-012811319-6 |
| Published: |
Elsevier
2017
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa36668 |
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| 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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| Keywords: |
Slope stability; System reliability analysis; Support vector machine regression model; Monte Carlo simulation; Critical slip surface |
| College: |
Faculty of Science and Engineering |
| Start Page: |
127 |
| End Page: |
143 |