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Prediction Method for Slurry Balance Shield Tunneling Speed Based on Mean Filtering & Denoising and XGBoost Algorithm
(1. Software College, Tongji University, Shanghai 201804; 2 Guangdong GDH Pearl River Delta Water Supply Co., Ltd, Guangzhou 511455) ; 3. College of Civil Engineering, Tongji University, Shanghai 200092; 4. School of Civil and Traffic Engineering, Shenzhen University, Shenzhen 518060)
Abstract In the process of slurry balance shield tunneling, the tunneling speed has a significant impact on cutter wear, synchronous grouting and shield attitude. A reasonable tunneling speed is of great significance for improving construction efficiency and reducing construction risks. The historical data collected by the PLC system is used to build an intelligent prediction model to predict the tunneling speed in real time, which is an important basis for the realization of unmanned shield and intelligent control in the future. Based on the time series data of tunneling parameters collected in the Pearl River Delta Water Resources Allocation Project, the tunneling speed is selected as the prediction target, the Pearson correlation analysis method is used to extract important characteristic parameters,the mean filtering method is used to denoise the time series data of characteristic parameters, to calculate the mean and the variance of the denoised series to form the feature vector, and to build the corresponding prediction model based on the XGBoost algorithm. The influence of data sets before and after denoising by mean filtering and different hyper-parameters in the XGBoost algorithm on the prediction performance of the model is discussed. The results show that under the 5-fold cross validation, a more accurate prediction model of the tunneling speed of the shield machine can be established by using the data after denoising processing with mean filtering. With the XGBoost algorithm, the prediction accuracy of the tunneling speed of the shield machine based on the denoised data set has reached 99.97%, and the prediction accuracy based on the non-denoised data set has also reached 99.94%, which is superior to the mainstream random forest algorithm, the SVR algorithm and the GBDT algorithm. The experimental results prove the noise reduction effect of the mean filtering method on the time series data and the feasibility of using XGBoost algorithm to predict the tunneling speed.
Abstract：
In the process of slurry balance shield tunneling, the tunneling speed has a significant impact on cutter wear, synchronous grouting and shield attitude. A reasonable tunneling speed is of great significance for improving construction efficiency and reducing construction risks. The historical data collected by the PLC system is used to build an intelligent prediction model to predict the tunneling speed in real time, which is an important basis for the realization of unmanned shield and intelligent control in the future. Based on the time series data of tunneling parameters collected in the Pearl River Delta Water Resources Allocation Project, the tunneling speed is selected as the prediction target, the Pearson correlation analysis method is used to extract important characteristic parameters,the mean filtering method is used to denoise the time series data of characteristic parameters, to calculate the mean and the variance of the denoised series to form the feature vector, and to build the corresponding prediction model based on the XGBoost algorithm. The influence of data sets before and after denoising by mean filtering and different hyper-parameters in the XGBoost algorithm on the prediction performance of the model is discussed. The results show that under the 5-fold cross validation, a more accurate prediction model of the tunneling speed of the shield machine can be established by using the data after denoising processing with mean filtering. With the XGBoost algorithm, the prediction accuracy of the tunneling speed of the shield machine based on the denoised data set has reached 99.97%, and the prediction accuracy based on the non-denoised data set has also reached 99.94%, which is superior to the mainstream random forest algorithm, the SVR algorithm and the GBDT algorithm. The experimental results prove the noise reduction effect of the mean filtering method on the time series data and the feasibility of using XGBoost algorithm to predict the tunneling speed.
DU Qingfeng1 ZHANG Shuangli1 ZHANG Chenxi1 LI Xuhui2 XIAO Yongsheng2 LI Xiaojun3
.Prediction Method for Slurry Balance Shield Tunneling Speed Based on Mean Filtering & Denoising and XGBoost Algorithm[J] MODERN TUNNELLING TECHNOLOGY, 2022,V59(6): 14-23
2022, Vol. 59 