A novel deep learning-based identification technology of cutting pile states during super-large diameter shield tunnelling

Accurately identifying the position and quantity of piles is critical for ensuring the safe tunnelling process in shield cutting pile projects. The vibration signals generated during the shield cutting pile process contain abundant information. To address the challenge of determining pile positions and quantities, this study proposes a method for the identification of strata based on vibration characteristics, integrating the dual advantages of knowledge-driven and data-driven approaches. The method includes a data processing module, a knowledge-driven module, a transformer-based model (MT), and a comprehensive evaluation module, and it has been validated in the Guangzhou Haizhu Bay shield tunnel project. The results show that the developed method achieves an accuracy of 99.56% in the identification of strata types, improving by 1.33%, 1.11%, and 14.16% compared to the MLP, RF, and LSTM models, respectively. As the number of cutting piles increases, the frequency of vibration signals gradually rises, while the amplitude shows no significant change. Based on this finding, the top five frequencies were used as input. Position encoding was employed to effectively learn the positional information of the frequency, enabling the MT model to achieve an accuracy of 65.71% in identifying multiple piles, improving by 10.51%, 19.14%, and 23.46% compared to the MLP, RF, and LSTM models, respectively. Comprehensive evaluation analysis indicates that this method demonstrates superior recall and weighted accuracy, highlighting its strong flexibility and applicability in engineering contexts.