Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

Due to the closed working environment of shield machines, the construction personnel cannot observe the construction geological environment, which seriously restricts the safety and efficiency of the tunnel-ing process. In this study, we present an enhanced multi-head self-attention convolution neural network (EMSACNN) with two-stage feature extraction for geological condition prediction of shield machine. Firstly, we select 30 important parameters according to statistical analysis method and the working prin-ciple of the shield machine. Then, we delete the non-working sample data, and combine 10 consecutive data as the input of the model. Thereafter, to deeply mine and extract essential and relevant features, we build a novel model combined with the particularity of the geological type recognition task, in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the cor-relation of geological information of adjacent working face of tunnel, and two-dimensional CNN (2dCNN) is utilized as the second feature extractor. The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tun-nel in Guangzhou, China. The results show that EMSACNN achieves at least 96% accuracy on the test sets of the two tunnels, and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor. Therefore, the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine, which is of great guiding significance to engineering practice.(c) 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).