01827nas a2200205   4500000000100000000000100001008004100002260001200043100001600055700001300071700001700084700001300101700001400114700002600128700001900154700001300173245007100186490000800257520135600265       2023                            d  c06/20231 aHongyu Chen1 aXinyi Li1 aZongbao Feng1 aLei Wang1 aYawei Qin1 aMirosław Skibniewski1 aZhen-Song Chen1 aYang Liu00aShield attitude prediction based on Bayesian-LGBM machine learning0 v6323 a<p>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.</p>