Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels
Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify t...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-02-01
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| Series: | Underground Space |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967424000850 |
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| author | Junjie Ma Tianbin Li Zhen Zhang Roohollah Shirani Faradonbeh Mostafa Sharifzadeh Chunchi Ma |
| author_facet | Junjie Ma Tianbin Li Zhen Zhang Roohollah Shirani Faradonbeh Mostafa Sharifzadeh Chunchi Ma |
| author_sort | Junjie Ma |
| collection | DOAJ |
| description | Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications. |
| format | Article |
| id | doaj-art-d738c76c224b4c9bb291016d53240cac |
| institution | Kabale University |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-d738c76c224b4c9bb291016d53240cac2024-12-18T08:50:56ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-02-0120140156Novel multifractal-based classification model for the quality grades of surrounding rock within tunnelsJunjie Ma0Tianbin Li1Zhen Zhang2Roohollah Shirani Faradonbeh3Mostafa Sharifzadeh4Chunchi Ma5College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China; WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie WA 6430, AustraliaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China; Corresponding authors.WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie WA 6430, Australia; Corresponding authors.WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie WA 6430, AustraliaWA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie WA 6430, AustraliaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, ChinaUnderstanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.http://www.sciencedirect.com/science/article/pii/S2467967424000850Surrounding rock classificationTunnel boring machineOperational parameterMultifractal characteristicsMultiple linear regression |
| spellingShingle | Junjie Ma Tianbin Li Zhen Zhang Roohollah Shirani Faradonbeh Mostafa Sharifzadeh Chunchi Ma Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels Underground Space Surrounding rock classification Tunnel boring machine Operational parameter Multifractal characteristics Multiple linear regression |
| title | Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels |
| title_full | Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels |
| title_fullStr | Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels |
| title_full_unstemmed | Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels |
| title_short | Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels |
| title_sort | novel multifractal based classification model for the quality grades of surrounding rock within tunnels |
| topic | Surrounding rock classification Tunnel boring machine Operational parameter Multifractal characteristics Multiple linear regression |
| url | http://www.sciencedirect.com/science/article/pii/S2467967424000850 |
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