Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance (EPB) shield tunnelling. Five artificial intelligence (AI) models based on machine and deep learning techniques—back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), long-short term memory (LSTM), and gated recurrent unit (GRU)—are used. Five geological and nine operational parameters that influence the advancing speed are considered. A field case of shield tunnelling in Shenzhen City, China is analyzed using the developed models. A total of 1000 field datasets are adopted to establish intelligent models. The prediction performance of the five models is ranked as GRU > LSTM > SVM > ELM > BPNN. Moreover, the Pearson correlation coefficient (PCC) is adopted for sensitivity analysis. The results reveal that the main thrust (MT), penetration (P), foam volume (FV), and grouting volume (GV) have strong correlations with advancing speed (AS). An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets. Finally, the prediction performances of the intelligent models and the empirical method are compared. The results reveal that all the intelligent models perform better than the empirical method