NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions

AI, vol. 6, pp. 127

Abstract

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 (p < 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.

Authors 4

1. Youliang Chen

   University of Shanghai for Science and Technology

   Affiliation as printed

   Department of Civil Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd., Shanghai 200093, China

2. Wencan Guan corresponding

   University of Shanghai for Science and Technology

   Affiliation as printed

   Department of Civil Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd., Shanghai 200093, China

3. Rafig Azzam Aachen Department of Engineering Geology and Hydrogeology

   RWTH Aachen University

   Affiliation as printed

   Department of Engineering Geology and Hydrogeology, RWTH Aachen University, Lochnerstr. 4-20 Haus A, D-52064 Aachen, Germany

4. Siyu Chen

   Shenzhen University

   Affiliation as printed

   Department of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China