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

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.