Predicting 3D Failure Envelopes of Caisson Foundations in Nonhomogeneous Clay: FELA, AdaBoost, SVM, and KNN
Article
| Article Title | Predicting 3D Failure Envelopes of Caisson Foundations in Nonhomogeneous Clay: FELA, AdaBoost, SVM, and KNN |
|---|---|
| ERA Journal ID | 200622 |
| Article Category | Article |
| Authors | Vichai, Katavut, Shiau, Jim, Tran, Duy Tan, Keawsawasvong, Suraparb and Jamsawang, Pitthaya |
| Journal Title | Indian Geotechnical Journal |
| Number of Pages | 26 |
| Year | 2025 |
| ISSN | 0971-9555 |
| 2277-3347 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s40098-025-01289-1 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s40098-025-01289-1 |
| Abstract | This study presents a machine learning-based framework for predicting failure envelopes of typical and modified caisson foundations under combined general loading (V, H, and M) in nonhomogeneous clays. Parametric analyses are conducted using 3D finite element limit analysis (FELA) with the Tresca failure criterion to evaluate the effects of embedment depth ratio (L/D) and nonuniform strength ratio (κ) on 2D and 3D failure envelopes and failure patterns of two caisson foundation types. Special emphasis is placed on 2D failure envelopes in the H-M space (H/suLD, M/suL2D) under varying vertical load levels (V/V0), considering full-tension conditions at the foundation–soil interface. To address the complexities of geotechnical stability modeling, machine learning techniques such as adaptive boosting (AdaBoost), support vector machines, and K-nearest neighbors are employed to develop surrogate models for constructing failure envelopes. Among these, the AdaBoost model demonstrates superior performance, achieving the highest accuracy (R2 = 98.9%) in predicting failure envelopes. This integration of machine learning and 3D FELA represents a robust and efficient approach to understand and model the behavior of caisson foundations under general loading conditions. |
| Keywords | Caisson; Failure envelope; Nonhomogeneous ; Machine learning ; General loading |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400502. Civil geotechnical engineering |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Thammasat University, Thailand |
| School of Engineering | |
| Ho Chi Minh City University of Transport, Vietnam | |
| King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand |
https://research.usq.edu.au/item/zy8xw/predicting-3d-failure-envelopes-of-caisson-foundations-in-nonhomogeneous-clay-fela-adaboost-svm-and-knn
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