Danial Jahed Armaghani^1,*, 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 >

Shengjun Deng^1,2,3, Haolin Chen¹, Xiaonan Gong², Jiajin Zhou², Xiangdong Hu⁴, Gang Jiang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 251-266, 2022, DOI:10.32604/cmes.2022.020388 - 02 June 2022

Abstract Freeze-sealing pipe roof method is applied in the Gongbei tunnel, which causes the ground surface uplift induced by frost heave. A frost heaving prediction approach based on the coefficient of cold expansion is proposed to simulate the ground deformation of the Gongbei tunnel. The coefficient of cold expansion in the model and the frost heaving rate from the frost heave test under the hydration condition can achieve a good correspondence making the calculation result closer to the actual engineering. The ground surface uplift along the lateral and longitudinal direction are respectively analyzed and compared with… More >