Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS
The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction qua...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/6612824 |
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| author | Baoping Zou Jianxiu Wang Zhanyou Luo Lisheng Hu |
| author_facet | Baoping Zou Jianxiu Wang Zhanyou Luo Lisheng Hu |
| author_sort | Baoping Zou |
| collection | DOAJ |
| description | The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology. |
| format | Article |
| id | doaj-art-5ef7718e489c465299db901b7cf3d00a |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-5ef7718e489c465299db901b7cf3d00a2025-08-20T03:34:22ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/66128246612824Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFISBaoping Zou0Jianxiu Wang1Zhanyou Luo2Lisheng Hu3School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaSchool of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaChina Railway No.2 Engineering Group Co., Ltd, Chengdu 610031, ChinaThe construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology.http://dx.doi.org/10.1155/2021/6612824 |
| spellingShingle | Baoping Zou Jianxiu Wang Zhanyou Luo Lisheng Hu Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS Geofluids |
| title | Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS |
| title_full | Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS |
| title_fullStr | Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS |
| title_full_unstemmed | Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS |
| title_short | Intelligent Control of Smooth Blasting Quality in Rock Tunnels Using BP-ANN, ENN, and ANFIS |
| title_sort | intelligent control of smooth blasting quality in rock tunnels using bp ann enn and anfis |
| url | http://dx.doi.org/10.1155/2021/6612824 |
| work_keys_str_mv | AT baopingzou intelligentcontrolofsmoothblastingqualityinrocktunnelsusingbpannennandanfis AT jianxiuwang intelligentcontrolofsmoothblastingqualityinrocktunnelsusingbpannennandanfis AT zhanyouluo intelligentcontrolofsmoothblastingqualityinrocktunnelsusingbpannennandanfis AT lishenghu intelligentcontrolofsmoothblastingqualityinrocktunnelsusingbpannennandanfis |