A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load
In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To...
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| Format: | Article |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Built Environment |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2022.962331/full |
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| author | Van Qui Lai Van Qui Lai Kongtawan Sangjinda Suraparb Keawsawasvong Alireza Eskandarinejad Vinay Bhushan Chauhan Worathep Sae-Long Suchart Limkatanyu |
| author_facet | Van Qui Lai Van Qui Lai Kongtawan Sangjinda Suraparb Keawsawasvong Alireza Eskandarinejad Vinay Bhushan Chauhan Worathep Sae-Long Suchart Limkatanyu |
| author_sort | Van Qui Lai |
| collection | DOAJ |
| description | In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To develop the set of training data in MARS, the lower and upper bound finite element limit analysis (FELA) is carried out to obtain the numerical results of the bearing capacity of a strip footing with the width of B. There are six considered dimensionless variables, including the geological strength index (GSI), the rock constant/yield parameter (mi), the dimensionless strength (γB/σci), the adhesion factor (α), load inclined angle from the vertical axis (β), and the eccentricity of load (e/B). A total of 5,120 FELA solutions of the bearing capacity factor (P/σciB) are obtained and used as a training data set. The influences of all dimensionless variables on the bearing capacity factors and the failure mechanisms are investigated and discussed in detail. The sensitivity analysis of these dimensionless variables is also examined. |
| first_indexed | 2024-12-10T06:16:26Z |
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| id | doaj.art-c37f5bc6554c4bfb9553a1feb35e0a6a |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-12-10T06:16:26Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-c37f5bc6554c4bfb9553a1feb35e0a6a2022-12-22T01:59:27ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622022-09-01810.3389/fbuil.2022.962331962331A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric loadVan Qui Lai0Van Qui Lai1Kongtawan Sangjinda2Suraparb Keawsawasvong3Alireza Eskandarinejad4Vinay Bhushan Chauhan5Worathep Sae-Long6Suchart Limkatanyu7Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamVietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, VietnamDepartment of Civil Engineering, Thammasat School of Engineering, Thammasat University, Bangkok, ThailandDepartment of Civil Engineering, Thammasat School of Engineering, Thammasat University, Bangkok, ThailandDepartment of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan, IranCivil Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, IndiaCivil Engineering Program, School of Engineering, University of Phayao, Muang Phayao, ThailandDepartment of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, ThailandIn this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To develop the set of training data in MARS, the lower and upper bound finite element limit analysis (FELA) is carried out to obtain the numerical results of the bearing capacity of a strip footing with the width of B. There are six considered dimensionless variables, including the geological strength index (GSI), the rock constant/yield parameter (mi), the dimensionless strength (γB/σci), the adhesion factor (α), load inclined angle from the vertical axis (β), and the eccentricity of load (e/B). A total of 5,120 FELA solutions of the bearing capacity factor (P/σciB) are obtained and used as a training data set. The influences of all dimensionless variables on the bearing capacity factors and the failure mechanisms are investigated and discussed in detail. The sensitivity analysis of these dimensionless variables is also examined.https://www.frontiersin.org/articles/10.3389/fbuil.2022.962331/fullbearing capacityHoek-Brownmarsrock massstrip footing |
| spellingShingle | Van Qui Lai Van Qui Lai Kongtawan Sangjinda Suraparb Keawsawasvong Alireza Eskandarinejad Vinay Bhushan Chauhan Worathep Sae-Long Suchart Limkatanyu A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load Frontiers in Built Environment bearing capacity Hoek-Brown mars rock mass strip footing |
| title | A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| title_full | A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| title_fullStr | A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| title_full_unstemmed | A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| title_short | A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| title_sort | machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load |
| topic | bearing capacity Hoek-Brown mars rock mass strip footing |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2022.962331/full |
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