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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...

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Published in: Handbook of Neural Computation
ISBN: 978-012811319-6
Published: Elsevier 2017
URI: https://cronfa.swan.ac.uk/Record/cronfa36668
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spelling 2017-11-08T14:31:31.8556147 v2 36668 2017-11-08 Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 2017-11-08 FGSEN 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. Book chapter Handbook of Neural Computation 127 143 Elsevier 978-012811319-6 Slope stability; System reliability analysis; Support vector machine regression model; Monte Carlo simulation; Critical slip surface 31 12 2017 2017-12-31 10.1016/B978-0-12-811318-9.00007-7 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2017-11-08T14:31:31.8556147 2017-11-08T14:28:55.7135444 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Subhadeep Metya 1 Tanmoy Mukhopadhyay 2 Sondipon Adhikari 3 Gautam Bhattacharya 4
title Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
spellingShingle Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
Sondipon Adhikari
title_short Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
title_full Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
title_fullStr Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
title_full_unstemmed Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
title_sort Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model
author_id_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3
author_id_fullname_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
author Sondipon Adhikari
author2 Subhadeep Metya
Tanmoy Mukhopadhyay
Sondipon Adhikari
Gautam Bhattacharya
format Book chapter
container_title Handbook of Neural Computation
container_start_page 127
publishDate 2017
institution Swansea University
isbn 978-012811319-6
doi_str_mv 10.1016/B978-0-12-811318-9.00007-7
publisher Elsevier
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
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description 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.
published_date 2017-12-31T03:45:59Z
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score 11.01297

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