Short-impending decision-making for TBM tunneling control based on loading phase data

The scientific decision-making of TBM boring phase tunneling parameters is of great significance for ensuring safe and efficient TBM tunneling. This study proposes a short-impending decision-making process framework for TBM tunneling control based on loading phase data. Firstly, the Pearson correlation coefficient method was used to calculate the correlation between the tunneling parameters of the loading phase and the boring phase, and the thrust F, torque T, rotational speed N, and penetration p with the highest correlation were obtained as the input parameters for the loading phase; Then, Improved Symmetric Geometric Mode Decomposition (ISGMD) was used to decompose the input parameters of the TBM loading phase, and the Improved Symmetric Geometry Component (ISGC) with high correlation coefficient was obtained; Subsequently, Composite Multiscale Permutation Entropy (CMPE) was used to calculate the characteristic entropy values of the loading phase input parameters’ ISGCs, which were used as the loading phase input variables, and the surrounding rock grade was used as the geological condition constraint input variable, and the boring phase TBM tunneling parameters of the previous tunneling cycle were used as the time-series input variables to construct a TBM tunneling data sample library; Afterwards, the Osprey-Cauchy Sparrow Search Algorithm (OCSSA) was used to globally optimize the hyperparameters of the Improved Temporal Convolutional Network (ITCN) model, obtain the optimal model hyperparameters, and construct the OCSSA-ITCN model; Finally, based on the OCSSA-ITCN model, the stable tunneling parameters F_s, T_s, N_s, and p_s of TBM were predicted. The average R², MAPE, and RMSE of the predicted results were 0.9460, 6.25%, and 235.39, respectively, indicating high prediction accuracy. In addition, the influence of different input variables on the prediction accuracy of the OCSSA-ITCN model was discussed, as well as the improvement efficiency of the OCSSA and ITCN algorithms, and the enhancement performance of the ISGMD and CMPE models. The rationality of the proposed short-term decision-making framework was further validated using the engineering verification dataset. In summary, the proposed TBM tunneling control short-impending decision-making process framework has good engineering applicability and can provide scientific auxiliary decision-making for drivers to select TBM tunneling parameters.