Deep learning-based prediction of steady surface settlement due to shield tunnelling

Abstract

Predicting ground movement produced by shield tunnelling in densely built urban areas is of practical significance. This study introduces an artificial intelligence method to predict the steady surface settlement caused by shield tunnelling. The proposed method comprises three models: a hybrid deep neural network model (HDNN), an error correction model (ECM), and an auxiliary model. Based on the settlement mechanism associated with tunnelling, the factors related to the ground movement were classified into five categories. The first four types of factors were used as the input parameters of the HDNN to predict steady surface settlement. The ECM considers unmeasurable factors. Five datasets from different subway lines were used to assess the performance and generalisation capacity of the proposed method. The results show that the proposed method outperforms classical deep neural networks as it considers the settlement mechanism and spatial and temporal characteristics of the input parameters.