## 5 Conclusions  
This paper aimed to identify the sandy and pebble formations excavated by the earth pressure balance shield machine. First, the numerical variation laws of cutterhead torque and thrust in single and a composite formations were analyzed and obtained using discrete element simulation of the cutterhead system's excavation. Then, the construction of the tunneling simulation test bench and the tunneling experiment in sandy and pebble formations were carried out. The discrete element simulation model was validated using experimental data. Finally, an excavation formation identification model was developed, and a method for identifying excavation formations using the GTNet network model was proposed. The conclusions are as follows:  
1. A discrete element earth pressure balance shield machine cutterhead system excavation model was established. Excavation simulations were conducted in single and composite formations. The thrust and torque of the cutterhead are directly proportional to the content of gravel and pebbles in sandy and pebble formations, and the content of pebbles has the greatest impact on the excavation parameters. The torque of the cutter head increases with an increase in the cutterhead rotational speed, and the thrust of the cutter head decreases with an increase in the cutterhead rotational speed but is not greatly affected by them. At the beginning of tunneling, the torque of the cutterhead decreases as the tunneling speed increases and remains in a certain range during stable tunneling. The thrust of the cutterhead increases as the tunneling speed accelerates.
2. Five types of sand and pebble formations were set up to conduct excavation simulation experiments, respectively. The data analysis of the torque and thrust of the cutterhead shows that the values of both increase with an increase in the content of gravel and pebbles in the formation. It can be seen that the torque and thrust of the cutterhead have a strong correlation. The discrete element model was verified by using experimental data, which is consistent with the experiment.
3. A recognition and classification model for excavated sand and gravel formations was established based on the GTNet network. After preprocessing the data, network training was conducted to find the optimal hyperparameters, and the accuracy rate was found to reach 99.3%. GTNet was compared with the classic Transformer, GRU, and LSTM network models, and GTNet had the highest accuracy rate. The precision rate, recall rate, and \(f1\) value show that the GTNet network model has the best performance. The ablation study shows that cutterhead torque and thrust give the most formation features for recognition. The proposed GTNet network model meets the recognition requirements of the earth pressure balance shield machine in the excavation of sandy and pebble formations.  
[PERSON] oversaw the project as the team leader, provided strategic guidance on research direction, and supervised the progress of the study. [PERSON] built the whole structure and wrote the manuscript. [PERSON]. ([PERSON]) conducted in-depth analysis with data investigation and optimized the methodology. [PERSON] contributed to data processing, indicator calculations, and visualization. [PERSON]. ([PERSON]) proposed the research framework and technical roadmap. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science foundation of China, grant number 62173238; Education Department Program of Liaoning Provincial, Grant number LJ222410153061, and LJ212410153041; Science and Technology Plan Project of Liaoning Province, grant number 2024-BSLH-248; Shenyang bureau of science and technology, Grant number No. 24-213-3-03.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are available from the corresponding authors on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.