Optimizing load-displacement prediction for bored piles with the 3mSOS algorithm and neural networks
Article
Article Title | Optimizing load-displacement prediction for bored piles with the 3mSOS algorithm and neural networks |
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ERA Journal ID | 4710 |
Article Category | Article |
Authors | Nguyen, Tan, Ly, Duy-Khuong, Shiau, Jim and Nguyen-Dinh, Phi |
Journal Title | Ocean Engineering |
Journal Citation | 304 |
Article Number | 117758 |
Number of Pages | 20 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0029-8018 |
1873-5258 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.oceaneng.2024.117758 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0029801824010965 |
Abstract | The study presents an innovative hybrid machine learning model tailored for predicting the load-displacement characteristics of bored piles, specifically those integral to high-rise buildings. Incorporating critical design parameters—diameter, length, Standard Penetration Test (SPT) indices, and effective overburden pressure—the model leverages a dataset of 1650 samples from static load tests in Vietnam. This hybrid approach integrates the Three Modified Symbiotic Organisms Search algorithm (3mSOS) with the Levenberg–Marquardt backpropagation neural network (LMNN) to establish the intricate relationship between these design parameters and the load-displacement response of the piles. Numerical results underscore the model's exceptional performance in accurately predicting the load-displacement behavior of bored piles. Rigorous validation employs an independent dataset derived from bidirectional pile load tests, affirming the model's reliability. A comprehensive sensitivity analysis provides valuable insights into the mechanisms governing load-bearing. Feature importance analysis and partial dependence plots reveal nuanced relationships among input variables and output behavior. The model's novelty lies in pioneering the application of advanced metaheuristic algorithms, notably 3mSOS, in pile foundations—a distinctive contribution to geotechnical engineering. This research holds significant promise for enhancing the efficiency and accuracy of pile design in high-rise buildings, thereby bolstering the overall reliability of foundation design. |
Keywords | Bored piles ; Load-displacement behavior ; Machine learning ; Hybrid model ; 3mSOS algorithm |
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 | Ton Duc Thang University, Vietnam |
Van Lang University, Viet Nam | |
School of Engineering |
https://research.usq.edu.au/item/z6227/optimizing-load-displacement-prediction-for-bored-piles-with-the-3msos-algorithm-and-neural-networks
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