Surrounding rock grade identification of deep TBM tunnel based on data decomposition and model fusion

Abstract

The automatic identification of surrounding rock grade is a key challenge in the TBM construction of deep tunnels. This study aims to develop an automatic identification system based on TBM ascending section tunneling data to provide accurate guidance for stable section tunneling. A total of 6734  m of per-second TBM tunneling data was collected, and a preprocessing method for null and outlier values was proposed. The TBM complete tunneling cycle was divided into empty pushing, ascending, stable, and descending sections. Based on improved ICEEMDAN and HWPE, the T and F curves of the ascending section were decomposed, feature entropy values of IMF components were extracted, and an IMF component HWPE sample database was constructed. A Stacking ensemble framework was developed, and the Stacking-BIGRU model achieved identification accuracies of 95.0 %, 100.0 %, 97.5 %, and 90.0 % for grade II, IIIa, IIIb, and IV rock, respectively, with an overall accuracy of 95.625 %. Compared with traditional EMD and PE, the improved ICEEMDAN and HWPE enhanced the overall accuracy by 13.33 % and 7.75 %, respectively. The proposed system can effectively extract feature information from ascending section tunneling data, enabling accurate surrounding rock grade identification.