Ada-attention mechanism for intelligent parameter optimization in TBM rock fragmentation: A deep learning approach

Intelligent geoengineering., vol. 2, pp. 192–215

Abstract

Tunnel Boring Machine (TBM) rock breaking parameter optimization is a technical challenge in underground engineering. Traditional numerical simulation methods have limitations in computational efficiency and accuracy when dealing with multi-parameter coupling optimization under complex geological conditions. This study proposes a deep learning method based on the Ada-Attention mechanism for predicting and optimizing parameters such as confining pressure, penetration depth, and cutting tool spacing in TBM rock breaking processes. The method employs a hybrid attention architecture that combines global window mechanisms with local sliding windows, reducing the computational complexity of traditional self-attention mechanisms from O(n²) to O(n(w+α)). Additionally, the Newton-Gauss optimization algorithm is introduced to improve the softmax normalization process, enhancing numerical stability and convergence performance. The research constructs a prediction framework covering a temperature range from 25°C to 500°C, using 800 experimental samples for model training and validation. Experimental results show that the Ada-Attention model achieves R² values of 0.92, 0.93, and 0.94 for torque, rolling force, and specific energy predictions respectively, obtaining 2-10 times computational speedup compared to traditional Transformer architectures. Generalization capability validation demonstrates that the model exhibits high prediction accuracy in soft sedimentary rock and medium sandstone (R²>0.95), maintains moderate performance levels in hard limestone and crystalline rock (R²=0.80-0.90), while prediction accuracy decreases in complex geological environments such as composite formations and fractured rock masses. This method provides a feasible technical solution for TBM parameter optimization under complex geological conditions.

Authors 4

1. Wencan Guan Aachen Department of Engineering Geology and Hydrogeology

   University of Shanghai for Science and Technology · Bauhaus-Universität Weimar · RWTH Aachen University

   Affiliation as printed

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

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

   Institute of Structural Mechanics (ISM), Bauhaus University Weimar,Geschwister-Scholl-Straße 8, 99423 Weimar, Germany

2. Suran Wang corresponding

   Tongji University · 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, PR China

   Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China

3. Youliang Chen

   University of Shanghai for Science and Technology · UNSW Sydney

   Affiliation as printed

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

   Department of Engineering Geology, School of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW 2052, Australia

4. Siyu Chen

   Shenzhen University

   Affiliation as printed

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