Bio-inspired deep learning for predicting offshore pile capacity under VHM loads: an xDeepFM-EAO framework
Article
| Article Title | Bio-inspired deep learning for predicting offshore pile capacity under VHM loads: an xDeepFM-EAO framework |
|---|---|
| ERA Journal ID | 213374 |
| Article Category | Article |
| Authors | Vichai, Katavut, Shiau, Jim, Tran, Duy Tan, Khajehzadeh, Mohammad, Keawsawasvong, Suraparb and Jamsawang, Pitthaya |
| Journal Title | Journal of Ocean Engineering and Marine Energy |
| Number of Pages | 43 |
| Year | 2025 |
| Publisher | Springer |
| Place of Publication | Germany |
| ISSN | 2198-6444 |
| 2198-6452 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s40722-025-00451-4 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s40722-025-00451-4 |
| Abstract | This study presents a hybrid predictive framework that integrates the eXtreme Deep Factorization Machine (xDeepFM) with the Enzyme Action Optimizer (EAO) to study the capacity of offshore pile foundations subjected to combined vertical-horizontal-moment (V-H-M) loading. The model is trained using a high-fidelity dataset consisting of 3,328 cases generated by Finite Element Limit Analysis (FELA), incorporating key dimensionless parameters such as the pile embedment ratio (L/D), soil heterogeneity index (κ), strength anisotropy ratio (re), normalized vertical load ratio (V/V₀), and load inclination angle (β). The proposed framework achieves excellent predictive performance (R2 > 0.99) on both the training and testing sets and effectively captures complex, nonlinear, and interdependent patterns within the data. Model interpretability, assessed using SHapley Additive exPlanations (SHAP) values and correlation analysis, reveals strong alignment with fundamental geotechnical principles, thereby enhancing transparency and trust in the predictions. Overall, the xDeepFM-EAO framework offers a robust, accurate, and computationally efficient tool for the design and analysis of offshore foundations subjected to multidirectional loading. |
| Keywords | Offshore pile foundation; Combined loading; Finite element limit analysis (FELA); xDeepFM; Enzyme action optimizer (EAO) |
| 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 | |
| Islamic Azad University, Iran | |
| King Mongkut’s University of Technology North Bangkok, Thailand |
https://research.usq.edu.au/item/100w6y/bio-inspired-deep-learning-for-predicting-offshore-pile-capacity-under-vhm-loads-an-xdeepfm-eao-framework
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