Probabilistic analysis of gravity retaining wall against bearing failure

Machine learning (ML) models have been extensively used in the stability check of gravity retaining wall. They are renowned as the most capable methods for predicting factor of safety (FOS) of gravity retaining wall against bearing failure. In this work, FOS against bearing is predicted based on ext...

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Main Authors: Mustafa, Rashid, Samui, Pijush, Kumari, Sunita, Mohamad, Edy Tonnizam, Bhatawdekar, Ramesh Murlidhar
Format: Article
Language:English
Published: Institute for Ionics 2023
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Online Access:http://eprints.utm.my/105381/1/EdyTonnizamMohamad2023_ProbabilisticAnalysisOfGravityRetainingWall.pdf
http://eprints.utm.my/105381/
http://dx.doi.org/10.1007/s42107-023-00697-z
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spelling my.utm.1053812024-04-24T06:45:33Z http://eprints.utm.my/105381/ Probabilistic analysis of gravity retaining wall against bearing failure Mustafa, Rashid Samui, Pijush Kumari, Sunita Mohamad, Edy Tonnizam Bhatawdekar, Ramesh Murlidhar TA Engineering (General). Civil engineering (General) Machine learning (ML) models have been extensively used in the stability check of gravity retaining wall. They are renowned as the most capable methods for predicting factor of safety (FOS) of gravity retaining wall against bearing failure. In this work, FOS against bearing is predicted based on extreme gradient boosting (XGBoost), random forest (RF) and deep neural network (DNN). To establish homogeneity and distribution of datasets, Anderson-Darling (AD) and Mann-Whitney U (M-W) tests are carried out, respectively. These three machine learning models are applied to 100 datasets by considering six influential input parameters for predicting FOS against bearing failure. The execution of the established machine learning models is assessed by several performance parameters. The obtained results from computational approach shows that DNN attained the best predictive performance with coefficient of determination (R 2) = 0.998 and root mean square error (RMSE) = 0.006 in the training phase and R 2 = 0.929 and RMSE = 0.053 in the testing phase. The models result are also analyzed by using rank analysis, regression error characteristics curve, and accuracy matrix. Sensitivity analysis is carried to know the relative importance of input variables. Institute for Ionics 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/105381/1/EdyTonnizamMohamad2023_ProbabilisticAnalysisOfGravityRetainingWall.pdf Mustafa, Rashid and Samui, Pijush and Kumari, Sunita and Mohamad, Edy Tonnizam and Bhatawdekar, Ramesh Murlidhar (2023) Probabilistic analysis of gravity retaining wall against bearing failure. Asian Journal of Civil Engineering, 24 (8). pp. 3099-3119. ISSN 1563-0854 http://dx.doi.org/10.1007/s42107-023-00697-z DOI : 10.1007/s42107-023-00697-z
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mustafa, Rashid
Samui, Pijush
Kumari, Sunita
Mohamad, Edy Tonnizam
Bhatawdekar, Ramesh Murlidhar
Probabilistic analysis of gravity retaining wall against bearing failure
description Machine learning (ML) models have been extensively used in the stability check of gravity retaining wall. They are renowned as the most capable methods for predicting factor of safety (FOS) of gravity retaining wall against bearing failure. In this work, FOS against bearing is predicted based on extreme gradient boosting (XGBoost), random forest (RF) and deep neural network (DNN). To establish homogeneity and distribution of datasets, Anderson-Darling (AD) and Mann-Whitney U (M-W) tests are carried out, respectively. These three machine learning models are applied to 100 datasets by considering six influential input parameters for predicting FOS against bearing failure. The execution of the established machine learning models is assessed by several performance parameters. The obtained results from computational approach shows that DNN attained the best predictive performance with coefficient of determination (R 2) = 0.998 and root mean square error (RMSE) = 0.006 in the training phase and R 2 = 0.929 and RMSE = 0.053 in the testing phase. The models result are also analyzed by using rank analysis, regression error characteristics curve, and accuracy matrix. Sensitivity analysis is carried to know the relative importance of input variables.
format Article
author Mustafa, Rashid
Samui, Pijush
Kumari, Sunita
Mohamad, Edy Tonnizam
Bhatawdekar, Ramesh Murlidhar
author_facet Mustafa, Rashid
Samui, Pijush
Kumari, Sunita
Mohamad, Edy Tonnizam
Bhatawdekar, Ramesh Murlidhar
author_sort Mustafa, Rashid
title Probabilistic analysis of gravity retaining wall against bearing failure
title_short Probabilistic analysis of gravity retaining wall against bearing failure
title_full Probabilistic analysis of gravity retaining wall against bearing failure
title_fullStr Probabilistic analysis of gravity retaining wall against bearing failure
title_full_unstemmed Probabilistic analysis of gravity retaining wall against bearing failure
title_sort probabilistic analysis of gravity retaining wall against bearing failure
publisher Institute for Ionics
publishDate 2023
url http://eprints.utm.my/105381/1/EdyTonnizamMohamad2023_ProbabilisticAnalysisOfGravityRetainingWall.pdf
http://eprints.utm.my/105381/
http://dx.doi.org/10.1007/s42107-023-00697-z
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score 13.18916