Prediction Model of Shield Segment Floating Process During Construction Based on Convolutional Neural Networks and Long Short-Term Memory

Abstract:

To realize the intelligent prediction of the floating process of shield segment during construction， the dynamic level was used to automatically monitor the floating process of shield segment， and an intelligent prediction model of segment floating process based on convolutional neural networks and long short-term memory （CNN-LSTM） deep learning algorithm was established. The results show that the floating stage of segment is in a shape of “ladder”， i.e.， the floating of segment mainly occurs during shield tunneling. In addition， the floating amount in the heading state is the largest， accounting for 75.24 % to 98.29 % of the peak value. The mean square error （MSE）， average absolute error （MAE） and determination coefficient R2on the training set are 0.038 7， 0.148 2， and 0.999 3 respectively， and 0.030 7， 0.138 9 and 0.801 9 on the test set. Compared with the back propagation （BP） model， the performance of the CNN-LSTM model in the training set and test set has been improved， and the improvement of the test set is more obvious， up to 89.71 %. The research results can provide a new idea for field measurement， prevention， and treatment of shield segment floating.

Key words:shield tunnel;segment uplift;floating process;intelligent prediction;automatic monitoring