Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines
Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tun...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/7060 |
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| author | Jianming Zhang Kebin Shi Hadelibieke Majiti Hongze Shan Tao Fu Renyi Shi Zhipeng Lu |
| author_facet | Jianming Zhang Kebin Shi Hadelibieke Majiti Hongze Shan Tao Fu Renyi Shi Zhipeng Lu |
| author_sort | Jianming Zhang |
| collection | DOAJ |
| description | Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model’s features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model’s input, while the top shield cylinder rod’s chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control. |
| first_indexed | 2024-03-11T02:49:04Z |
| format | Article |
| id | doaj.art-bfd3424ac0d44929a339ed4fd50ac0b2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:49:04Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bfd3424ac0d44929a339ed4fd50ac0b22023-11-18T09:08:24ZengMDPI AGApplied Sciences2076-34172023-06-011312706010.3390/app13127060Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring MachinesJianming Zhang0Kebin Shi1Hadelibieke Majiti2Hongze Shan3Tao Fu4Renyi Shi5Zhipeng Lu6College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaXinjiang Erqis River Investment and Development (Group) Co., Ltd., Urumqi 830000, ChinaCollege of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaRock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model’s features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model’s input, while the top shield cylinder rod’s chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control.https://www.mdpi.com/2076-3417/13/12/7060surrounding rock excavatability classificationtunnel boring machinewater conveyance tunneldeep forestmachine learningTOPSIS |
| spellingShingle | Jianming Zhang Kebin Shi Hadelibieke Majiti Hongze Shan Tao Fu Renyi Shi Zhipeng Lu Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines Applied Sciences surrounding rock excavatability classification tunnel boring machine water conveyance tunnel deep forest machine learning TOPSIS |
| title | Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines |
| title_full | Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines |
| title_fullStr | Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines |
| title_full_unstemmed | Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines |
| title_short | Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines |
| title_sort | study on the classification and identification methods of surrounding rock excavatability based on the rock breaking performance of tunnel boring machines |
| topic | surrounding rock excavatability classification tunnel boring machine water conveyance tunnel deep forest machine learning TOPSIS |
| url | https://www.mdpi.com/2076-3417/13/12/7060 |
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