The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.

Accurate, rapid and effective analysis of rock drillability is very important for mining, civil and petroleum engineering. In this study, a method of rock drillability evaluation based on drilling process monitoring (DPM) parameters is proposed by using the field drilling test data. The revolutions per minute (N), thrust, torque and rate of penetration (ROP) were recorded in real time. Then, the two-dimensional regression analysis was utilized to investigate the relationships between the drilling parameters, and the three-dimensional regression analysis was used to establish models of ROP and specific energy (SE), in which the N-F-ROP, N-T-ROP and the improved SE model were obtained. In addition, the random forest (RF) and support vector machine combined with genetic algorithm (GA-SVM) were applied to predict rock drillability. Finally, a prediction model of uniaxial compressive strength (UCS) was established based on the SE and drillability index, I_d. The results show that both regression models and prediction models have good performance, which can provide important guidance and a data source for field drilling and excavation processes.

To reduce subjectivity and uncertainty when maintaining suitable earth pressure in earth pressure shields that can prevent heave or collapse, many prediction models using machine learning algorithms were proposed, but little research into the effects of other parameters on earth pressure has been undertaken, and soil conditioning parameters are always ignored. To establish a model with thorough parameters and probe into influences of other parameters, multiple machine learning algorithms were attempted. Given the accuracy, diversity and functions, random forest (RF), LightGBM and Attention-back-propagation neural network (Attention-BPNN) were further analyzed. Then, two RF models were compared in this research, one with soil conditioning parameters and the other without. Meanwhile, a case study was utilized to verify the reliability of the model. Finally, the feature importance of three models was compared and the variation rules of the most four important features were discussed by controlling variates. The results showed that soil conditioning parameters delivered a significant reduction in the prediction error. The case study demonstrated that the proposed model can satisfy engineering requirements. More earth pressure should give priority to increasing propulsion pressure, advance rate, and reducing foam air flow, rotational speed of screw conveyor, and vice versa.