Shield attitude prediction based on Bayesian-LGBM machine learning

TytułShield attitude prediction based on Bayesian-LGBM machine learning
Publication TypeJournal Article
Rok publikacji2023
AutorzyChen H, Li X, Feng Z, Wang L, Qin Y, Skibniewski MJ, Chen Z-S, Liu Y
JournalInformation Sciences
Start Page105-129
Date Published06/2023
Słowa kluczoweShield attitude; Shield construction parameters; Prediction and control; Machine learning; Bayesian-LGBM model

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: (1) The developed the 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 support the effective shield attitude control by continuously adjusting the shield construction parameters. the proposed attitude guidance control method based on the Bayesian-LGBM can be used to provide a reference for actual shield attitude applications and other similar problems.


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Data aktualizacji: 20/12/2023 - 13:53; autor zmian: Żaneta Deka (