Stability of rectangular tunnels in cohesive-frictional soil under surcharge loading using isogeometric analysis and Bayesian neural networks
Article
| Article Title | Stability of rectangular tunnels in cohesive-frictional soil under surcharge loading using isogeometric analysis and Bayesian neural networks |
|---|---|
| ERA Journal ID | 22083 |
| Article Category | Article |
| Authors | Nguyen, Minh-Toan, Bui, Tram-Ngoc, Shiau, Jim, Nguyen, Tan and Nguyen, Thoi-Trung |
| Journal Title | Advances in Engineering Software |
| Journal Citation | 201 |
| Article Number | 103861 |
| Number of Pages | 27 |
| Year | 2025 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0965-9978 |
| 1873-5339 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.advengsoft.2024.103861 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0965997824002680 |
| Abstract | This study evaluates the stability of rectangular tunnels in cohesive-frictional soils under surcharge loading using a combination of IsoGeometric Analysis and artificial neural networks. A dataset of 12,946 samples was generated automatically to analyze a wide range of soil profiles and tunnel geometries. Stability solutions were derived using IsoGeometric Analysis coupled with second-order cone programming, enabling precise and efficient assessments of ultimate surcharge loading. A key contribution of this study is the development of a closed-form solution through a Bayesian regularized neural network, which significantly improves accuracy compared to existing methods. Advanced data visualization techniques, including two- and three-dimensional partial dependency plots, were used to reveal complex relationships among design parameters. Sensitivity analyses provided valuable insights for optimizing tunnel designs, enhancing decision-making processes in geotechnical engineering. This study aims to equip engineers with practical tools for designing rectangular tunnels in real-world applications. |
| Keywords | Bayesian regularized feed-forward neural; network; Closed-form solution; Upper bound limit analysis; Ultimate surcharge loading; Partial dependency plots |
| 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/zqxxx/stability-of-rectangular-tunnels-in-cohesive-frictional-soil-under-surcharge-loading-using-isogeometric-analysis-and-bayesian-neural-networks
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