TY  - EJOU
AU  - Armaghani, Danial Jahed 
AU  - Liu, Zida 
AU  - Khabbaz, Hadi 
AU  - Fattahi, Hadi 
AU  - Li, Diyuan 
AU  - Afrazi, Mohammad 

TI  - Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 141
IS  - 3
SN  - 1526-1506

AB  - Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This 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 characteristics. These techniques are able to provide a good relationship between input(s) and output parameters; hence, obtaining a high level of accuracy. To 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 development. The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties. To evaluate model performance, ranking systems and Taylor diagrams were employed. CatBoost emerged as the most accurate model during training and testing, with R<sup>2</sup> scores of 0.927 and 0.861, respectively. However, during validation, XGBoost demonstrated superior performance with an R<sup>2</sup> of 0.713. Despite these variations, all tree-based models showed promising accuracy in predicting TBM performance, providing valuable insights for similar projects in the future.
KW  - TBM performance; penetration rate; tunnel construction; tree-based models; rock mass and material properties

DO  - 10.32604/cmes.2024.052210