Rock mass type prediction for tunnel boring machine using a novel semi-supervised method
•A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor and a feature classifier.•Geological feature extractor and classifier are obtained based on SSAE and DNN, respectively.•A set of data preproce...
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| Published in: | Measurement : journal of the International Measurement Confederation Vol. 179; p. 109545 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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London
Elsevier Ltd
01.07.2021
Elsevier Science Ltd |
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | •A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor and a feature classifier.•Geological feature extractor and classifier are obtained based on SSAE and DNN, respectively.•A set of data preprocessing methods is proposed for the cleaning of the big machine data.•The proposed method outperforms other commonly adopted supervised methods.
Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful for safe and efficient tunneling. Since soft methods can use on-site data to predict geological conditions, they are getting more and more attention. However, there is an imbalance between the machine data and geological data, and current soft methods can only utilize limited machine data with geological labels, limiting the performance of the model. To make full use of the massive unlabeled data and limited labeled data, a novel semi-supervised method is proposed to establish the rock mass type prediction model. In the first step, twenty machine parameters are selected as inputs, and the data preprocessing is performed. Thereafter, a geological feature extractor is established based on the stacked sparse autoencoder and unlabeled machine data. Finally, a feature classifier is obtained based on the deep neural network and labeled geological features to realize the prediction of rock mass type. The on-site data collected from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method. The results indicate that the unsupervised stacked sparse autoencoder is capable of extracting geological features, and the proposed stacked sparse autoencoder and deep neural network-based semi-supervised method outperforms commonly adopted supervised methods. Its classification performance (F-measure) is 13.84%, 10.29%, 8.71%, 5.23% and 5.13% higher than the support vector machine-based, decision tree-based, K-nearest neighbor-based, random forest-based and deep neural network-based methods, respectively. Therefore, the proposed semi-supervised method can predict the rock mass types ahead of the tunnel face more accurately than the current supervised soft methods. |
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| AbstractList | Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful for safe and efficient tunneling. Since soft methods can use on-site data to predict geological conditions, they are getting more and more attention. However, there is an imbalance between the machine data and geological data, and current soft methods can only utilize limited machine data with geological labels, limiting the performance of the model. To make full use of the massive unlabeled data and limited labeled data, a novel semi-supervised method is proposed to establish the rock mass type prediction model. In the first step, twenty machine parameters are selected as inputs, and the data preprocessing is performed. Thereafter, a geological feature extractor is established based on the stacked sparse autoencoder and unlabeled machine data. Finally, a feature classifier is obtained based on the deep neural network and labeled geological features to realize the prediction of rock mass type. The on-site data collected from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method. The results indicate that the unsupervised stacked sparse autoencoder is capable of extracting geological features, and the proposed stacked sparse autoencoder and deep neural network-based semi-supervised method outperforms commonly adopted supervised methods. Its classification performance (F-measure) is 13.84%, 10.29%, 8.71%, 5.23% and 5.13% higher than the support vector machine-based, decision tree-based, K-nearest neighbor-based, random forest-based and deep neural network-based methods, respectively. Therefore, the proposed semi-supervised method can predict the rock mass types ahead of the tunnel face more accurately than the current supervised soft methods. •A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor and a feature classifier.•Geological feature extractor and classifier are obtained based on SSAE and DNN, respectively.•A set of data preprocessing methods is proposed for the cleaning of the big machine data.•The proposed method outperforms other commonly adopted supervised methods. Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful for safe and efficient tunneling. Since soft methods can use on-site data to predict geological conditions, they are getting more and more attention. However, there is an imbalance between the machine data and geological data, and current soft methods can only utilize limited machine data with geological labels, limiting the performance of the model. To make full use of the massive unlabeled data and limited labeled data, a novel semi-supervised method is proposed to establish the rock mass type prediction model. In the first step, twenty machine parameters are selected as inputs, and the data preprocessing is performed. Thereafter, a geological feature extractor is established based on the stacked sparse autoencoder and unlabeled machine data. Finally, a feature classifier is obtained based on the deep neural network and labeled geological features to realize the prediction of rock mass type. The on-site data collected from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method. The results indicate that the unsupervised stacked sparse autoencoder is capable of extracting geological features, and the proposed stacked sparse autoencoder and deep neural network-based semi-supervised method outperforms commonly adopted supervised methods. Its classification performance (F-measure) is 13.84%, 10.29%, 8.71%, 5.23% and 5.13% higher than the support vector machine-based, decision tree-based, K-nearest neighbor-based, random forest-based and deep neural network-based methods, respectively. Therefore, the proposed semi-supervised method can predict the rock mass types ahead of the tunnel face more accurately than the current supervised soft methods. |
| ArticleNumber | 109545 |
| Author | Yu, Honggan Qin, Chengjin Xiao, Dengyu Liu, Chengliang Sun, Hao Tao, Jianfeng |
| Author_xml | – sequence: 1 givenname: Honggan surname: Yu fullname: Yu, Honggan – sequence: 2 givenname: Jianfeng surname: Tao fullname: Tao, Jianfeng email: jftao@sjtu.edu.cn – sequence: 3 givenname: Chengjin surname: Qin fullname: Qin, Chengjin email: qinchengjin@sjtu.edu.cn – sequence: 4 givenname: Dengyu surname: Xiao fullname: Xiao, Dengyu – sequence: 5 givenname: Hao surname: Sun fullname: Sun, Hao – sequence: 6 givenname: Chengliang surname: Liu fullname: Liu, Chengliang |
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| Keywords | Tunnel boring machine Rock mass type prediction Stacked sparse autoencoder Semi-supervised learning Data preprocessing |
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| Snippet | •A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor... Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful... |
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| SubjectTerms | Artificial neural networks Boring machines Data preprocessing Decision trees Feature extraction Geology Neural networks Onsite Prediction models Rock mass type prediction Rock masses Rocks Semi-supervised learning Stacked sparse autoencoder Subway tunnels Support vector machines Tunnel boring machine Tunnel construction Tunnels Underground construction |
| Title | Rock mass type prediction for tunnel boring machine using a novel semi-supervised method |
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