Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks
Abstract This study investigates the uplift capacity of circular anchors embedded in frictional-cohesive soils under surcharge. The analysis focuses on three critical stability factors F c , F q , and F γ using Terzaghi’s principle of superposition to evaluate ultimate bearing capacity. These factor...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98945-6 |
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| author | Tran Vu-Hoang Tan Nguyen Jim Shiau Hung-Thinh Pham-Tran Trung Nguyen-Thoi |
| author_facet | Tran Vu-Hoang Tan Nguyen Jim Shiau Hung-Thinh Pham-Tran Trung Nguyen-Thoi |
| author_sort | Tran Vu-Hoang |
| collection | DOAJ |
| description | Abstract This study investigates the uplift capacity of circular anchors embedded in frictional-cohesive soils under surcharge. The analysis focuses on three critical stability factors F c , F q , and F γ using Terzaghi’s principle of superposition to evaluate ultimate bearing capacity. These factors are influenced by the soil’s internal friction angle, the geometric ratio of anchor depth to diameter, and the interface roughness between the anchor and soil. Three predictive models for these stability factors are developed using advanced computational methods, including finite element limit analysis (FELA) with adaptive meshing and Kolmogorov-Arnold Networks (KAN). This research is the first to apply KAN in anchor behavior studies, demonstrating its enhanced ability to model complex data relationships compared to artificial neural networks (ANN). Additionally, a closed-form solution for stability factors is derived through KAN, providing an efficient method for predicting bearing capacity. The optimized models exhibit high coefficient of determination (R²) values and low root mean square errors (RMSE) for training and testing datasets. Sensitivity analysis validates the robustness of the proposed models. These findings advance the understanding of circular anchors’ bearing capacity in frictional-cohesive soils, offering practical design insights for various soil conditions. |
| format | Article |
| id | doaj-art-ca7102fb5b8e4f8ba0d891fe9f939660 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ca7102fb5b8e4f8ba0d891fe9f9396602025-08-20T03:14:03ZengNature PortfolioScientific Reports2045-23222025-04-0115112610.1038/s41598-025-98945-6Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networksTran Vu-Hoang0Tan Nguyen1Jim Shiau2Hung-Thinh Pham-Tran3Trung Nguyen-Thoi4Faculty of Civil Engineering, Ton Duc Thang UniversitySmart Computing in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang UniversitySchool of Engineering, University of Southern QueenslandFaculty of Civil Engineering, Ton Duc Thang UniversityLaboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang UniversityAbstract This study investigates the uplift capacity of circular anchors embedded in frictional-cohesive soils under surcharge. The analysis focuses on three critical stability factors F c , F q , and F γ using Terzaghi’s principle of superposition to evaluate ultimate bearing capacity. These factors are influenced by the soil’s internal friction angle, the geometric ratio of anchor depth to diameter, and the interface roughness between the anchor and soil. Three predictive models for these stability factors are developed using advanced computational methods, including finite element limit analysis (FELA) with adaptive meshing and Kolmogorov-Arnold Networks (KAN). This research is the first to apply KAN in anchor behavior studies, demonstrating its enhanced ability to model complex data relationships compared to artificial neural networks (ANN). Additionally, a closed-form solution for stability factors is derived through KAN, providing an efficient method for predicting bearing capacity. The optimized models exhibit high coefficient of determination (R²) values and low root mean square errors (RMSE) for training and testing datasets. Sensitivity analysis validates the robustness of the proposed models. These findings advance the understanding of circular anchors’ bearing capacity in frictional-cohesive soils, offering practical design insights for various soil conditions.https://doi.org/10.1038/s41598-025-98945-6Uplift capacityFrictional-cohesive soilsTerzaghi stability factorsKolmogorov-Arnold networks (KAN)Closed-form solutionSensitivity analysis |
| spellingShingle | Tran Vu-Hoang Tan Nguyen Jim Shiau Hung-Thinh Pham-Tran Trung Nguyen-Thoi Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks Scientific Reports Uplift capacity Frictional-cohesive soils Terzaghi stability factors Kolmogorov-Arnold networks (KAN) Closed-form solution Sensitivity analysis |
| title | Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks |
| title_full | Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks |
| title_fullStr | Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks |
| title_full_unstemmed | Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks |
| title_short | Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks |
| title_sort | predicting the uplift capacity of circular anchors in frictional cohesive soils using kolmogorov arnold networks |
| topic | Uplift capacity Frictional-cohesive soils Terzaghi stability factors Kolmogorov-Arnold networks (KAN) Closed-form solution Sensitivity analysis |
| url | https://doi.org/10.1038/s41598-025-98945-6 |
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