Symbolic regression based prediction of anisotropic closure in deep tunnels

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@Article{CarrilloGuayacan:2024:compgeo,

• author = "Lina-Maria Guayacan-Carrillo and Jean Sulem",
• title = "Symbolic regression based prediction of anisotropic closure in deep tunnels",
• journal = "Computers and Geotechnics",
• year = "2024",
• volume = "171",
• pages = "106355",
• keywords = "genetic algorithms, genetic programming, Tunnel excavation, Convergence measurements, Anisotropic deformation, Symbolic regression, Machine learning approach",
• ISSN = "0266-352X",
• URL = "https://www.sciencedirect.com/science/article/pii/S0266352X2400291X",
• DOI = "doi:10.1016/j.compgeo.2024.106355",
• abstract = "This work investigates the applicability of Genetic Programming with Symbolic Regression to analyse the closure evolution of tunnels during and after excavation. Special attention is given to anisotropic closure evolution, which depends on the anisotropy of the initial stress state and the intrinsic anisotropy of the rock mass formation. A methodology is proposed that takes into account the information recorded during excavation to train the algorithm and find a free-form simple mathematical expression that captures the closure evolution over time. The proposed methodology is applied to two case studies of deep tunnels with high anisotropic convergence evolution. The results obtained show that this approach performs well with the small dataset used in this work. The proposed approach is an interesting tool to improve the understanding and prediction of the ground response",

}

Genetic Programming entries for Lina-Maria Guayacan-Carrillo Jean Sulem

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