Portal de Periódicos da CAPES

Shield attitude prediction based on Bayesian-LGBM machine learning

2023; Elsevier BV; Volume: 632; Linguagem: Inglês

10.1016/j.ins.2023.03.004

1872-6291

Hongyu Chen, Xinyi Li, Zongbao Feng, Lei Wang, Yawei Qin, Mirosław J. Skibniewski, Zhen‐Song Chen, Yang Liu,

Tunneling and Rock Mechanics

Effective shield attitude control is essential for the quality and safety of shield construction. The traditional shield attitude control method is manual control based on a driver's experience, which has the defects of hysteresis and poor reliability. This research proposes an intelligent method to predict the shield attitude based on a Bayesian-light gradient boosting machine (LGBM) model. The constructed model includes 29 parameters that impact the shield attitude and 6 parameters that represent the shield attitude. The developed the Bayesian-LGBM model can predict the shield attitude and support shield attitude control by adjusting construction parameters and conducting iterative prediction. Guiyang rail transit line 3 is selected as a case study to verify the effectiveness of the proposed method. The results indicate that: (1) The developed Bayesian-LGBM model is able to effectively predict the shield attitude; (2) The importance ranking can clarify the key construction parameters that should be controlled; (3) The proposed method enables supporting the effective shield attitude control by continuously adjusting the shield construction parameters. The proposed attitude guidance control method based on the proposed Bayesian-LGBM model can be used to provide a reference for actual shield attitude applications and other similar problems.