Abstract
The prediction of cutterhead torque of earth pressure balance (EPB) shield machine is mainly studied. First, the idea of shield tunneling stage division is proposed. The process of shield tunneling from start to stop (or pause) is divided into start-up and stationary driving stages. Using the change point detection method based on linear regression, the separation points between start-up stage and stationary driving stage are identified from the original construction data, and the datasets of the two stages are extracted, respectively. Then, for the start-up stage, the linear regression method is suggested for the cutterhead torque prediction, since there is a strong linear correlation between the key parameters such as the cutterhead torque and the thrust force. Meanwhile, for the stationary driving stage, considering the fact that the key parameters vary smoothly and show obvious inertia, the long short-term memory (LSTM) network method can be used to establish the relationship model between cutterhead torque and other key parameters, such as the thrust force. Through the test experiments of construction data in Zhengzhou, Luoyang, and Dalian shield projects, the results show that the proposed segmented modeling method possesses good adaptability and the cutterhead torque prediction model has high prediction accuracy.
Highlights
Torque of the cutterhead is one of the key parameters for the tunnel boring machine (TBM), which maintains the cutterhead to rotate and cut the soil continuously, and is the basic parameter for equipment energy efficiency control and safety state monitoring
In [14], Wang et al studied the prediction of cutterhead torque based on the real data of Pine diversion and water supply project in Jilin Province of China, where nonlinear support vector regression (NSVR) is Discrete Dynamics in Nature and Society employed, and cutterhead torque is taken as the output and some operation parameters as inputs
Take the data from Rings 550–556 of Hui-Shang section of the tunnel project Line 4 in Zhengzhou as an example, and the data from the first six rings are used as training data and the last ring as testing data. e estimated linear regression model for cutterhead torque is obtained as follows: y −0.13 + 0.09x1 + 0.06x2 − 0.74x3 + 1.79x4 − 0.48x5 − 0.16x6 + 0.54x7, (6)
Summary
Torque of the cutterhead is one of the key parameters for the tunnel boring machine (TBM), which maintains the cutterhead to rotate and cut the soil continuously, and is the basic parameter for equipment energy efficiency control and safety state monitoring. In [14], Wang et al studied the prediction of cutterhead torque based on the real data of Pine diversion and water supply project in Jilin Province of China, where nonlinear support vector regression (NSVR) is Discrete Dynamics in Nature and Society employed, and cutterhead torque is taken as the output and some operation parameters as inputs All of these algorithm models, such as artificial neural network (ANN), support vector machine (SVM), and random forest models (RFM), are difficult to explain because of their complex nonlinear structure and are misleading to understand the relationship between the response and the predictors [15,16,17,18].
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