Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network
Water inrush and mud inrush pose a serious threat to safe construction in underground engineering, but it is very difficult to determine the safe distance of outburst prevention rock mass. Based on deep-buried tunnel engineering, this paper proposes a method to determine the safe distance from water...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2023/6800788 |
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| _version_ | 1832546654119526400 |
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| author | Zhensheng Cao Jiazhu Liu Shaoqiang Zhang Bowen Li Zhenhu Zhang Zhongjie Wang ChenChen Jiang |
| author_facet | Zhensheng Cao Jiazhu Liu Shaoqiang Zhang Bowen Li Zhenhu Zhang Zhongjie Wang ChenChen Jiang |
| author_sort | Zhensheng Cao |
| collection | DOAJ |
| description | Water inrush and mud inrush pose a serious threat to safe construction in underground engineering, but it is very difficult to determine the safe distance of outburst prevention rock mass. Based on deep-buried tunnel engineering, this paper proposes a method to determine the safe distance from water inrush disaster between the tunnel and the fault fracture zone. This method combines numerical modeling and backpropagation (BP) neural network. By means of numerical simulation, the internal influence law of the water pressure, lateral pressure coefficient, fault fracture zone width, and tunnel burial depth on the surrounding rock is analyzed. The evolution law of the minimum safe strata thickness under a single variable and the correlation degree between minimum safe strata thickness and various disaster factors are revealed. Based on the BP neural network analysis of the simulation results and the measured data of Wulaofeng Tunnel, the calculated values of safe strata thicknesses of fault fracture zone (F10) were determined. The results show that the thickness of the safe rock strata increases with increasing water pressure, lateral pressure coefficient, fault fracture zone width, and tunnel burial depth. The minimum safe thickness of F10 of the tunnel ranges from 7.1 m to 7.4 m, and 10 m is reserved in the actual project. The calculated results are consistent with the reserved thickness value in the construction. This conclusion can provide a reference for similar projects. |
| format | Article |
| id | doaj-art-348867261ec84633a9f2abdb4b5a8734 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-348867261ec84633a9f2abdb4b5a87342025-02-03T06:47:30ZengWileyGeofluids1468-81232023-01-01202310.1155/2023/6800788Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural NetworkZhensheng Cao0Jiazhu Liu1Shaoqiang Zhang2Bowen Li3Zhenhu Zhang4Zhongjie Wang5ChenChen Jiang6Power China Road Bridge Group Co.School of Civil & Resource EngineeringPower China Road Bridge Group Co.School of Civil & Resource EngineeringSchool of Civil & Resource EngineeringPower China Road Bridge Group Co.Power China Road Bridge Group Co.Water inrush and mud inrush pose a serious threat to safe construction in underground engineering, but it is very difficult to determine the safe distance of outburst prevention rock mass. Based on deep-buried tunnel engineering, this paper proposes a method to determine the safe distance from water inrush disaster between the tunnel and the fault fracture zone. This method combines numerical modeling and backpropagation (BP) neural network. By means of numerical simulation, the internal influence law of the water pressure, lateral pressure coefficient, fault fracture zone width, and tunnel burial depth on the surrounding rock is analyzed. The evolution law of the minimum safe strata thickness under a single variable and the correlation degree between minimum safe strata thickness and various disaster factors are revealed. Based on the BP neural network analysis of the simulation results and the measured data of Wulaofeng Tunnel, the calculated values of safe strata thicknesses of fault fracture zone (F10) were determined. The results show that the thickness of the safe rock strata increases with increasing water pressure, lateral pressure coefficient, fault fracture zone width, and tunnel burial depth. The minimum safe thickness of F10 of the tunnel ranges from 7.1 m to 7.4 m, and 10 m is reserved in the actual project. The calculated results are consistent with the reserved thickness value in the construction. This conclusion can provide a reference for similar projects.http://dx.doi.org/10.1155/2023/6800788 |
| spellingShingle | Zhensheng Cao Jiazhu Liu Shaoqiang Zhang Bowen Li Zhenhu Zhang Zhongjie Wang ChenChen Jiang Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network Geofluids |
| title | Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network |
| title_full | Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network |
| title_fullStr | Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network |
| title_full_unstemmed | Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network |
| title_short | Study on the Safety Thickness of Rock Strata for Outburst Prevention in Deep Tunnel Based on Numerical Modeling and Backpropagation Neural Network |
| title_sort | study on the safety thickness of rock strata for outburst prevention in deep tunnel based on numerical modeling and backpropagation neural network |
| url | http://dx.doi.org/10.1155/2023/6800788 |
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