A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays
The uplift capacity of pipeline systems in geotechnical engineering is influenced by internal loading and external factors, making it a significant consideration in pipeline design problems. Previous research has conducted experimental tests and numerical solutions to investigate the relationship be...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2024-03-01
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| Series: | Journal of Pipeline Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667143323000392 |
| _version_ | 1797202943152226304 |
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| author | Van Qui Lai Khamnoy Kounlavong Suraparb Keawsawasvong Truong Son Bui Ngoc Thi Huynh |
| author_facet | Van Qui Lai Khamnoy Kounlavong Suraparb Keawsawasvong Truong Son Bui Ngoc Thi Huynh |
| author_sort | Van Qui Lai |
| collection | DOAJ |
| description | The uplift capacity of pipeline systems in geotechnical engineering is influenced by internal loading and external factors, making it a significant consideration in pipeline design problems. Previous research has conducted experimental tests and numerical solutions to investigate the relationship between force and displacement or the resistance of pipelines in numerous soil media. This paper proposes a machine-learning regression technique to predict the uplift capacity of buried pipelines in anisotropic clays with parametric analysis. Specifically, the Multivariate Adaptive Regression Spline (MARS) is employed to establish the relationship between input parameters, namely the depth ratio (H/D), anisotropic strength ratio (re), load inclination (β), overburden factor (γH/Suc), adhesion factor (α), and the output uplift resistance (N) obtained from the finite element limit analysis (FELA), utilizing the AUS material model integrated with the OptumG2 commercial program. Furthermore, the sensitivity analysis outcome shows the embedded depth ratio is the most critical parameter, followed by the anisotropic strength ratio, overburden factor, load inclination, and adhesion factor. Additionally, the shear velocity field contours show that when the depth ratio and the load inclination increase, the dissipation of shear changes. Design data visualizations, tables, graph contours, and empirical equations are created and can be utilized to aid in the development of practical designs. |
| first_indexed | 2024-04-24T08:11:28Z |
| format | Article |
| id | doaj.art-47b2521a47a047c8904eb08e9de15a5a |
| institution | Directory Open Access Journal |
| issn | 2667-1433 |
| language | English |
| last_indexed | 2024-04-24T08:11:28Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Journal of Pipeline Science and Engineering |
| spelling | doaj.art-47b2521a47a047c8904eb08e9de15a5a2024-04-17T04:50:13ZengKeAi Communications Co. Ltd.Journal of Pipeline Science and Engineering2667-14332024-03-0141100147A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic claysVan Qui Lai0Khamnoy Kounlavong1Suraparb Keawsawasvong2Truong Son Bui3Ngoc Thi Huynh4Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam; Corresponding author.Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, ThailandResearch Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, ThailandFaculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, VietnamFaculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, VietnamThe uplift capacity of pipeline systems in geotechnical engineering is influenced by internal loading and external factors, making it a significant consideration in pipeline design problems. Previous research has conducted experimental tests and numerical solutions to investigate the relationship between force and displacement or the resistance of pipelines in numerous soil media. This paper proposes a machine-learning regression technique to predict the uplift capacity of buried pipelines in anisotropic clays with parametric analysis. Specifically, the Multivariate Adaptive Regression Spline (MARS) is employed to establish the relationship between input parameters, namely the depth ratio (H/D), anisotropic strength ratio (re), load inclination (β), overburden factor (γH/Suc), adhesion factor (α), and the output uplift resistance (N) obtained from the finite element limit analysis (FELA), utilizing the AUS material model integrated with the OptumG2 commercial program. Furthermore, the sensitivity analysis outcome shows the embedded depth ratio is the most critical parameter, followed by the anisotropic strength ratio, overburden factor, load inclination, and adhesion factor. Additionally, the shear velocity field contours show that when the depth ratio and the load inclination increase, the dissipation of shear changes. Design data visualizations, tables, graph contours, and empirical equations are created and can be utilized to aid in the development of practical designs.http://www.sciencedirect.com/science/article/pii/S2667143323000392Uplift resistancePipelineAUS clayFELAMARS |
| spellingShingle | Van Qui Lai Khamnoy Kounlavong Suraparb Keawsawasvong Truong Son Bui Ngoc Thi Huynh A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays Journal of Pipeline Science and Engineering Uplift resistance Pipeline AUS clay FELA MARS |
| title | A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| title_full | A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| title_fullStr | A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| title_full_unstemmed | A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| title_short | A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| title_sort | machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays |
| topic | Uplift resistance Pipeline AUS clay FELA MARS |
| url | http://www.sciencedirect.com/science/article/pii/S2667143323000392 |
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