NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions
This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. The framework resolves critical limitations of conventional experience-dri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/6/127 |
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| author | Youliang Chen Wencan Guan Rafig Azzam Siyu Chen |
| author_facet | Youliang Chen Wencan Guan Rafig Azzam Siyu Chen |
| author_sort | Youliang Chen |
| collection | DOAJ |
| description | This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. The framework resolves critical limitations of conventional experience-driven approaches that inadequately address the nonlinear coupling between the spatial heterogeneity of rock mass parameters and mechanical system responses. Three principal innovations are introduced: (1) a hardware-compatible sparse attention architecture achieving O(n) computational complexity while preserving high-fidelity geological feature extraction capabilities; (2) an adaptive kernel function optimization mechanism that reduces confidence interval width by 41.3% through synergistic integration of boundary likelihood-driven kernel selection with Chebyshev inequality-based posterior estimation; and (3) a physics-enhanced modelling methodology combining non-Hertzian contact mechanics with eddy field evolution equations. Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (<i>p</i> < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations. |
| format | Article |
| id | doaj-art-1aedb92c32fd40e89ec1cfa99cf2832f |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-1aedb92c32fd40e89ec1cfa99cf2832f2025-08-20T03:30:24ZengMDPI AGAI2673-26882025-06-016612710.3390/ai6060127NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological ConditionsYouliang Chen0Wencan Guan1Rafig Azzam2Siyu Chen3Department of Civil Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd., Shanghai 200093, ChinaDepartment of Civil Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd., Shanghai 200093, ChinaDepartment of Engineering Geology and Hydrogeology, RWTH Aachen University, Lochnerstr. 4-20 Haus A, D-52064 Aachen, GermanyDepartment of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaThis study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. The framework resolves critical limitations of conventional experience-driven approaches that inadequately address the nonlinear coupling between the spatial heterogeneity of rock mass parameters and mechanical system responses. Three principal innovations are introduced: (1) a hardware-compatible sparse attention architecture achieving O(n) computational complexity while preserving high-fidelity geological feature extraction capabilities; (2) an adaptive kernel function optimization mechanism that reduces confidence interval width by 41.3% through synergistic integration of boundary likelihood-driven kernel selection with Chebyshev inequality-based posterior estimation; and (3) a physics-enhanced modelling methodology combining non-Hertzian contact mechanics with eddy field evolution equations. Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (<i>p</i> < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations.https://www.mdpi.com/2673-2688/6/6/127machine learningTBM excavationgeological explorationtunnel constructionneural network |
| spellingShingle | Youliang Chen Wencan Guan Rafig Azzam Siyu Chen NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions AI machine learning TBM excavation geological exploration tunnel construction neural network |
| title | NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions |
| title_full | NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions |
| title_fullStr | NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions |
| title_full_unstemmed | NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions |
| title_short | NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions |
| title_sort | nsa chg an intelligent prediction framework for real time tbm parameter optimization in complex geological conditions |
| topic | machine learning TBM excavation geological exploration tunnel construction neural network |
| url | https://www.mdpi.com/2673-2688/6/6/127 |
| work_keys_str_mv | AT youliangchen nsachganintelligentpredictionframeworkforrealtimetbmparameteroptimizationincomplexgeologicalconditions AT wencanguan nsachganintelligentpredictionframeworkforrealtimetbmparameteroptimizationincomplexgeologicalconditions AT rafigazzam nsachganintelligentpredictionframeworkforrealtimetbmparameteroptimizationincomplexgeologicalconditions AT siyuchen nsachganintelligentpredictionframeworkforrealtimetbmparameteroptimizationincomplexgeologicalconditions |