Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods
In aeolian sand, the mechanical behavior of helical anchors involves complex performance evolution mechanisms that are not yet fully understood. This study employs a multi-scale integrated approach combining field tests, numerical simulations, and machine learning to systematically investigate the e...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Soils and Foundations |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0038080625001143 |
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| author | Yongping Li Songzhao Qu Jing Bai Dongming Yang Sangtian Hu Lefu Di Ruiyuan Han Yijin Wu Yuan Xiang Dapeng Wang Yi Zhang Yonghua Guo Zhe Zhang |
| author_facet | Yongping Li Songzhao Qu Jing Bai Dongming Yang Sangtian Hu Lefu Di Ruiyuan Han Yijin Wu Yuan Xiang Dapeng Wang Yi Zhang Yonghua Guo Zhe Zhang |
| author_sort | Yongping Li |
| collection | DOAJ |
| description | In aeolian sand, the mechanical behavior of helical anchors involves complex performance evolution mechanisms that are not yet fully understood. This study employs a multi-scale integrated approach combining field tests, numerical simulations, and machine learning to systematically investigate the evolution laws of the bearing behavior of helical anchors. The results indicate: (1) The critical embedment depth threshold for helical anchors in aeolian sand is H = 5D; beyond this threshold, the load direction effect can be neglected. (2) Multi-plate helical anchors exhibit significant geometrically nonlinear superposition behavior. Dense spacing (S/D < 4) produces notable stress superposition effects (η = 1.15–1.32), whereas wide spacing (S/D ≥ 4) results in independent bearing units (η = 0.97–1.03). (3) The XGBoost machine learning model identifies the internal friction angle, anchor plate diameter, and embedment depth ratio as the most influential features affecting bearing capacity. Based on these control parameters, predictive equations for the bearing capacity coefficient Nq and soil lateral friction coefficient Ku were developed, with predictions showing excellent agreement with experimental data. This provides engineers with a reliable analytical framework for performance-based design. The study not only deepens the understanding of the behavioral mechanisms of helical piles in aeolian sand but also offers practical solutions for geotechnical engineering practice. |
| format | Article |
| id | doaj-art-1ad8d4c1ecbc4f4395e90695cd8ccfae |
| institution | Kabale University |
| issn | 2524-1788 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soils and Foundations |
| spelling | doaj-art-1ad8d4c1ecbc4f4395e90695cd8ccfae2025-08-24T05:11:15ZengElsevierSoils and Foundations2524-17882025-09-0165510168010.1016/j.sandf.2025.101680Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methodsYongping Li0Songzhao Qu1Jing Bai2Dongming Yang3Sangtian Hu4Lefu Di5Ruiyuan Han6Yijin Wu7Yuan Xiang8Dapeng Wang9Yi Zhang10Yonghua Guo11Zhe Zhang12Inner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaHenan University of Urban Construction, Henan, Pingdingshan 467036, China; Corresponding author.Inner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaHenan University of Urban Construction, Henan, Pingdingshan 467036, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaHenan University of Urban Construction, Henan, Pingdingshan 467036, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Inner Mongolia Autonomous Region, Inner Mongolia Autonomous Region 010020, ChinaHenan University of Urban Construction, Henan, Pingdingshan 467036, ChinaPowerChina Henan Electric Power Survey and Design Institute Co., Ltd., Henan, Zhengzhou 450007, ChinaHenan University of Urban Construction, Henan, Pingdingshan 467036, ChinaIn aeolian sand, the mechanical behavior of helical anchors involves complex performance evolution mechanisms that are not yet fully understood. This study employs a multi-scale integrated approach combining field tests, numerical simulations, and machine learning to systematically investigate the evolution laws of the bearing behavior of helical anchors. The results indicate: (1) The critical embedment depth threshold for helical anchors in aeolian sand is H = 5D; beyond this threshold, the load direction effect can be neglected. (2) Multi-plate helical anchors exhibit significant geometrically nonlinear superposition behavior. Dense spacing (S/D < 4) produces notable stress superposition effects (η = 1.15–1.32), whereas wide spacing (S/D ≥ 4) results in independent bearing units (η = 0.97–1.03). (3) The XGBoost machine learning model identifies the internal friction angle, anchor plate diameter, and embedment depth ratio as the most influential features affecting bearing capacity. Based on these control parameters, predictive equations for the bearing capacity coefficient Nq and soil lateral friction coefficient Ku were developed, with predictions showing excellent agreement with experimental data. This provides engineers with a reliable analytical framework for performance-based design. The study not only deepens the understanding of the behavioral mechanisms of helical piles in aeolian sand but also offers practical solutions for geotechnical engineering practice.http://www.sciencedirect.com/science/article/pii/S0038080625001143Helical pileAeolian sandBearing mechanismMachine learningDesign method |
| spellingShingle | Yongping Li Songzhao Qu Jing Bai Dongming Yang Sangtian Hu Lefu Di Ruiyuan Han Yijin Wu Yuan Xiang Dapeng Wang Yi Zhang Yonghua Guo Zhe Zhang Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods Soils and Foundations Helical pile Aeolian sand Bearing mechanism Machine learning Design method |
| title | Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods |
| title_full | Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods |
| title_fullStr | Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods |
| title_full_unstemmed | Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods |
| title_short | Experimental and numerical study on helical piles in aeolian sand: bearing behavior and design methods |
| title_sort | experimental and numerical study on helical piles in aeolian sand bearing behavior and design methods |
| topic | Helical pile Aeolian sand Bearing mechanism Machine learning Design method |
| url | http://www.sciencedirect.com/science/article/pii/S0038080625001143 |
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