Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning

Abstract

The perception of surrounding rock geological conditions ahead the tunnel face is essential for TBM safe and efficient tunnelling. This paper developed a perception approach of surrounding rock class based on TBM operational big data and combined unsupervised-supervised learning. In data preprocessing, four data mining techniques (i.e., Z-score, K-NN, Kalman filtering, and wavelet packet decomposition) were used to detect outliers, substitute outliers, suppress noise, and extract features, respectively. Then, GMM was used to revise the original surrounding rock class through clustering TBM load parameters and performance parameters in view of the shortcomings of the HC method in the TBM-excavated tunnel. After that, five various ensemble learning classification models were constructed to identify the surrounding rock class, in which model hyper-parameters were automatically tuned by Bayes optimization. In order to evaluate model performance, balanced accuracy, Kappa, F1-score, and training time were taken into account, and a novel multi-metric comprehensive ranking system was designed. Engineering application results indicated that LightGBM achieved the most superior performance with the highest comprehensive score of 6.9066, followed by GBDT (5.9228), XGBoost (5.4964), RF (3.7581), and AdaBoost (0.9946). Through the weighted purity reduction algorithm, the contributions of input features on the five models were quantitatively analyzed. Finally, the impact of class imbalance on model performance was discussed using the ADASYN algorithm, showing that eliminating class imbalance can further improve the model's perception ability.