Danial Jahed Armaghani1,*, Zida Liu2, Hadi Khabbaz1, Hadi Fattahi3, Diyuan Li2, Mohammad Afrazi4
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210
- 31 October 2024
Abstract 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)… More >
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