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Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

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Computer Modeling in Engineering & Sciences 2024年第12期141卷 2421-2451页

作者： Danial Jahed Armaghani Zida Liu Hadi Khabbaz Hadi Fattahi Diyuan Li mohammad afrazi School of Civil and Environmental Engineering University of Technology Sydney(UTS)UltimoNSW 2007Australia School of Resources and Safety Engineering Central South UniversityChangsha410083China Faculty of Earth Sciences Engineering Arak University of TechnologyArak38181-46763Iran Department of Mechanical Engineering New Mexico Institute of Mining and TechnologySocorroNM 87801USA

Tunnel Boring Machines(TBMs)are vital for tunnel and underground construction due to their high safety and *** predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing *** study investigates the effectiveness of tree-based machine learning models,including Random Forest,Extremely Randomized Trees,Adaptive Boosting Machine,Gradient Boosting Machine,Extreme Gradient Boosting Machine(XGBoost),Light Gradient Boosting Machine,and CatBoost,in predicting the Penetration Rate(PR)of TBMs by considering rock mass and material *** techniques are able to provide a good relationship between input(s)and output parameters;hence,obtaining a high level of *** do that,a comprehensive database comprising various rock mass and material parameters,including Rock Mass Rating,Brazilian Tensile Strength,and Weathering Zone,was utilized for model *** practical application of these models was assessed with a new dataset representing diverse rock mass and material *** evaluate model performance,ranking systems and Taylor diagrams were *** emerged as the most accurate model during training and testing,with R2 scores of 0.927 and 0.861,***,during validation,XGBoost demonstrated superior performance with an R2 of *** these variations,all tree-based models showed promising accuracy in predicting TBM performance,providing valuable insights for similar projects in the future.

关键词： TBM performance penetration rate tunnel construction tree-based models rock mass and material properties

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