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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institute for Ionics
2023
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/105381/1/EdyTonnizamMohamad2023_ProbabilisticAnalysisOfGravityRetainingWall.pdf |
| _version_ | 1811132118766452736 |
|---|---|
| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-09-23T23:57:14Z |
| format | Article |
| id | utm.eprints-105381 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-23T23:57:14Z |
| publishDate | 2023 |
| publisher | Institute for Ionics |
| record_format | dspace |
| spelling | utm.eprints-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 |
| 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 |
| title | 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_short | Probabilistic analysis of gravity retaining wall against bearing failure |
| title_sort | probabilistic analysis of gravity retaining wall against bearing failure |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/105381/1/EdyTonnizamMohamad2023_ProbabilisticAnalysisOfGravityRetainingWall.pdf |
| work_keys_str_mv | AT mustafarashid probabilisticanalysisofgravityretainingwallagainstbearingfailure AT samuipijush probabilisticanalysisofgravityretainingwallagainstbearingfailure AT kumarisunita probabilisticanalysisofgravityretainingwallagainstbearingfailure AT mohamadedytonnizam probabilisticanalysisofgravityretainingwallagainstbearingfailure AT bhatawdekarrameshmurlidhar probabilisticanalysisofgravityretainingwallagainstbearingfailure |