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.

Sandy dolomite strata are widely distributed in the diversion tunnel of the Yuxi section of the Water Diversion Project in Central Yunnan (WDPCY). The water-sand inrush is the main failure phenomenon in the construction of sandy dolomite tunnel, which has seriously and commonly threatened the safety of the Chenaju (CNJ) tunnel in the WDPCY. In this paper, we established the discriminant matrix of the water-sand inrush risk assessment by consulting the experts, including the designers and construction personnel of the CNJ tunnel. Then, the risk assessment of water-sand inrush in the CNJ tunnel is carried out based on the proposed Fuzzy-AHP method. The results show that (1) a hierarchical model for risk assessment of water-sand inrush in sandy dolomite tunnel is constructed by criterion and index including thirteen factors. (2) The main criterion layer factors affecting water-sand inrush in the sandy dolomite tunnel are dolomite’s sandy degree and water yield. (3) The risk grade of water-sand inrush in the sections of YX49 + 089∼YX49 + 099 and YX51 + 637∼YX51 + 647 in the CNJ tunnel is V, which indicates very high risk. The assessment results are consistent with the actual engineering situation, which indicate the proposed method can provide the reference for the risk management in a similar project.

Purpose: To introduce a digital workflow for the prediction of facial aesthetics, especially in patients with dentation deformity caused by maxillofacial trauma.Methods: Cone-beam computed tomography (CBCT) and three-dimensional facial scans of patients with radiographic prostheses were collected. The aforementioned data were uploaded to ProPlan CMF software and merged to generate a virtual patient with craniofacial hard tissue, realistic facial soft tissue, and remaining dentition. The radiographic prostheses were scanned to form a digital cast, which was fitted with its CBCT image to create the virtual prostheses. Postoperative facial soft tissue was simulated according to the movement of the virtual prostheses. An appropriate virtual diagnostic prosthesis plan was selected by the patient and dentist. Subsequently, prosthetically driven implant guide and restoration were designed and fabricated.Conclusions: A virtual patient was successfully constructed. A 4-mm protrusion of the virtual prosthesis was chosen. Subsequently, implant surgery was performed, and dental prostheses were fabricated based on this location. The fusion of the postoperative facial scan and preoperative facial prediction was found to be coincident. This technique can effectively predict facial aesthetic features of patients with maxillofacial trauma, facilitate communication with patients, reduce chairside time, and guide the multidisciplinary design of implant placement and restoration fabrication.

The excavation of deep tunnel in rock mass undergoes complex loading and unloading stress paths, resulting in rib spalling, flaking, and even severe rockburst disasters. Based on the variation law of the stress path of the surrounding rock, laboratory tests of rock mechanics are designed, and the deformation and strength behavior of marble with different initial confining pressure and unloading rates are systematically studied. By introducing strain increment, the characteristic stress, and the dilatancy index, the rock’s dilatancy and brittleness under different unloading conditions are quantitatively analyzed. During unloading, the energy transformation mechanism of rock is described, and the law of deformation and failure is discussed based on characteristic energy. The rock failure strength fitting formula is given by applying the Mogi–Coulomb criterion and elastic strain energy criterion. The advantages of the elastic strain energy criterion are theoretically explained. This study shows that comprehensive consideration of the complex stress paths, confining pressure levels, and the loading‐unloading rates of surrounding rock is an effective way to accurately study unloading rock characteristics. The results can provide theoretical basis for stability analysis of high‐stress underground engineering.

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.