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 tunneling process. In this study, we present an enhanced multi-head self-attention convolution neura...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987122001724 |
| _version_ | 1797701908111032320 |
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| author | Chengjin Qin Guoqiang Huang Honggan Yu Ruihong Wu Jianfeng Tao Chengliang Liu |
| author_facet | Chengjin Qin Guoqiang Huang Honggan Yu Ruihong Wu Jianfeng Tao Chengliang Liu |
| author_sort | Chengjin Qin |
| collection | DOAJ |
| description | 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 tunneling 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 principle 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 correlation 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 tunnel 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. |
| first_indexed | 2024-03-12T04:42:26Z |
| format | Article |
| id | doaj.art-e7d3f994a0c0450e94e593e90f6b5339 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T04:42:26Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-e7d3f994a0c0450e94e593e90f6b53392023-09-03T09:33:33ZengElsevierGeoscience Frontiers1674-98712023-03-01142101519Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extractionChengjin Qin0Guoqiang Huang1Honggan Yu2Ruihong Wu3Jianfeng Tao4Chengliang Liu5Corresponding author.; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDue 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 tunneling 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 principle 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 correlation 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 tunnel 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.http://www.sciencedirect.com/science/article/pii/S1674987122001724Geological information predictionShield machineEnhanced multi-head self-attentionCNN |
| spellingShingle | Chengjin Qin Guoqiang Huang Honggan Yu Ruihong Wu Jianfeng Tao Chengliang Liu Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction Geoscience Frontiers Geological information prediction Shield machine Enhanced multi-head self-attention CNN |
| title | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction |
| title_full | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction |
| title_fullStr | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction |
| title_full_unstemmed | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction |
| title_short | Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction |
| title_sort | geological information prediction for shield machine using an enhanced multi head self attention convolution neural network with two stage feature extraction |
| topic | Geological information prediction Shield machine Enhanced multi-head self-attention CNN |
| url | http://www.sciencedirect.com/science/article/pii/S1674987122001724 |
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