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 |
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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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