A multistage model for rapid identification of geological features in shield tunnelling.

Decision-making on shield construction parameters depends on timely and accurate geological condition feedback. Real-time mastering of geological condition around the shield during tunnelling is necessary to achieve safe and efficient construction. This paper proposes a Rapidly Geological Features Identification (RGFI) method that balances the model's generalizability and the accuracy of geological identification. First, a k-means algorithm is used to redefine the stratum based on the key mechanical indexes of strata. An XGBoost model is then used to determine the stratum composition of the excavation face based on the tunnelling parameters. If the result is compound strata, a deep neural network with an attention mechanism is used to predict the percentage of each stratum. The attention mechanism assigns weights to the features of the tunnelling parameters according to the stratum composition. The simulation results in the interval between Qian-Zhuang and Ke-Ning Road of Nanjing Metro show that the method can effectively determine the geological conditions on the excavation face. Furthermore, the method was used in the Hangzhou-Shaoxing intercity railroad tunnel project, where the 'ZhiYu' self-driving shield was used for tunnelling control. It helped the 'ZhiYu' shield to adjust the construction parameters quickly and improve the safety and quality of the project.