Predicting failure envelopes of skirted spudcan footings under combined loads using ACO–optimized extremely randomized trees
Article
Article Title | Predicting failure envelopes of skirted spudcan footings under combined loads using ACO–optimized extremely randomized trees |
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ERA Journal ID | 3795 |
Article Category | Article |
Authors | Vichai, Katavut, Tran, Duy Tan, Shiau, Jim, Keawsawasvong, Suraparb and Jamsawang, Pitthaya |
Journal Title | Marine Structures |
Journal Citation | 104 |
Article Number | 103890 |
Number of Pages | 20 |
Year | 2025 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0951-8339 |
1873-4170 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.marstruc.2025.103890 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0951833925001133 |
Abstract | Skirted spudcan foundations are widely employed in offshore geotechnical engineering due to their enhanced penetration capability and superior load resistance in soft clay soils. In this study, the undrained failure envelope of skirted spudcan subjected to combined vertical, horizontal, and moment (VHM) loading is investigated using Finite Element Limit Analysis (FELA). A total of 624 simulations are performed in OptumG3, systematically varying the embedment ratio (L/D), soil strength heterogeneity (κ), vertical load mobilization (V/V0), and loading limit for the FELA (β) to construct the VHM failure envelope in non-homogeneous clay. To complement the numerical approach and mitigate computational intensity, a machine learning model is developed using Extremely Randomized Trees (ET) optimized via Ant Colony Optimization (ACO). The resulting ET-ACO model demonstrates excellent agreement with the FELA outcomes, achieving R² values exceeding 0.998. Feature importance analysis highlights the FELA loading limit (β) and embedment ratio (L/D) as the most influential parameters governing failure capacity. This data-driven methodology provides a reliable and effective alternative for evaluating offshore foundation performance, as it not only accelerates the prediction of failure envelopes but also significantly reduces computational costs. |
Keywords | ET-ACO; Skirted spudcan; FELA; Failure envelope; Machine learning |
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 |
Ho Chi Minh City University of Transport, Vietnam | |
School of Engineering | |
King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand |
https://research.usq.edu.au/item/zy8xq/predicting-failure-envelopes-of-skirted-spudcan-footings-under-combined-loads-using-aco-optimized-extremely-randomized-trees
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