Attitude-Predictive Control of Large-Diameter Shield Tunneling: PCA-SVR Machine Learning Algorithm Application in a Case Study of the Zhuhai Xingye Express Tunnel

Abstract

In order to realize effective attitude-predictive control during large-diameter shield tunneling, this study established an intelligent framework for attitude prediction. Specifically, a principal component analysis–support vector regression (PCA-SVR) hybrid model was constructed, based on principal component analysis. The principal component analysis method was used to mine the relevant input parameters and reduce the accompanying data noise. SVR used statistical learning and structural risk minimization to overcome data overfitting. Taking a large-diameter shield tunnel, the Zhuhai Xingye Express Tunnel, as an example, the proposed PCA-SVR model was validated by considering tunnel excavation parameters, geometric parameters, and geological parameters. At the same time, the correlation coefficient was used to analyze the relationship between input parameters and attitude parameters. The results show that the propulsion cylinder pressure is an important factor affecting the trajectory of attitude motion. The geometrical and geological parameters of the shield have a strong correlation with the attitude parameters. The attitude parameters predicted by the model are within the range of the corresponding monitoring data. The high prediction accuracy verifies that the proposed PCA-SVR hybrid model can accurately predict the attitude parameters during shield tunneling. The prediction framework can be used for reference for the attitude-prediction control of shields in similar projects.