Journal of Northeastern University(Natural Science) ›› 2024, Vol. 45 ›› Issue (12): 1769-1777.DOI: 10.12068/j.issn.1005-3026.2024.12.012

• Resources & Civil Engineering • Previous Articles    

Prediction of Soil Conditioners for Sandy Soil Shield Based on Optuna-XGBoost

Xiu-mei CAO1, Wen ZHAO1(), Zhi-guo WANG1, Peng HE2   

  1. 1.School of Resources & Civil Engineering,Northeastern University,Shenyang 110819,China
    2.Shenyang Shield Equipment Engineering Co. ,Ltd,Shenyang 110819,China.
  • Received:2023-07-12 Online:2024-12-10 Published:2025-03-18
  • Contact: Wen ZHAO

Abstract:

Soil conditioning is an effective measure to solve the problems in the construction process of earth pressure balance (EPB) shield, such as cutter clogging and cutter abrasions. The use of machine learning models to predict soil conditioners varying with geological conditions can not only reduce the aforementioned construction risks, but also make up for the lag in determining the amount of modifier by test method. Based on the shield project of Shenyang Metro Line 4, the excavation data of 1 396 ring are preprocessed, and torque penetration index (TPI) and field penetration index (FPI) are used as the criteria for the soil conditioning effect to select good datasets. The Optuna-XGBoost model is established to predict the soil conditioners. The results show that Optuna algorithm owns obvious advantages over other methods in hyperparameters optimization. Compared with the other five prediction models, Optuna-XGBoost model owns higher accuracy under changeable geological conditions.

Key words: earth pressure balance shield, soil conditioning, tunneling parameters, geological parameter, prediction model

CLC Number: