@article{bibcite_15924,
  author = {Hongyu Chen and Xinyi Li and Zongbao Feng and Lei Wang and Yawei Qin and Miros{\l}aw Skibniewski and Zhen-Song Chen and Yang Liu},
  title = {Shield attitude prediction based on Bayesian-LGBM machine learning},
  abstract = {<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{\textquoteright}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>
},
  year = {2023},
  journal = {Information Sciences},
  volume = {632},
  chapter = {105-129},
  month = {06/2023},
  doi = {DOI: 10.1016/j.ins.2023.03.004},
  language = {eng},
}