Slope deformation monitoring and analysis are significant in the geological survey of hydraulic engineering. However, predicting future slope deformation is a vital and challenging task for engineers. The accurate estimation of slope displacement is required for the risk assessment of slope stability. This study was conducted using slope deformation data obtained by interferometric synthetic aperture radar. Five typical points of the slope in different zones were selected to establish the prediction model. Based on the observed data, a prediction model based on long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) was proposed. Firstly, ARIMA and LSTM models were used separately to predict slope deformation. Root mean square error, mean absolute error, and R² were used to evaluate the performance of the models, and the results showed that LSTM is more effective than ARIMA. It denotes that the LSTM model can catch the trend in the data sequence with time, and ARIMA is good at predicting the bias in the stationary data sequence. Then, the predictions of ARIMA were added to the original data while the new data were fed to the LSTM model. For most data points, our LSTM-ARIMA model achieved good performance, indicating that the model is robust in slope deformation prediction. The effectiveness of the proposed LSTM-ARIMA model will enable engineers to take corresponding measures to prevent accidents before landslides occur.

In tunnel boring machine (TBM) construction, the advance rate is a crucial parameter that affects the TBM driving efficiency, project schedule, and construction cost. During the operation process, various types of indicators that are monitored in real-time can help to control the advance rate of TBM. Although some studies have already been carried out in advance rate prediction, the research is almost all based on statistical methods and shallow machine learning algorithms, thereby having difficulties in dealing with a very large amount of monitored data and in modeling the time-dependent characteristics of the parameters. To solve this problem, a deep learning model is proposed based on the CNN architecture, bidirectional Long Short-Term Memory module, and the attention mechanism, which is called the CNN-Bi-LSTM-Attention model. In the first step, the monitored data is processed, and the CNN architecture is adopted to extract features from the data sequence. Then the Bi-LSTM module is adopted to obtain the time-dependent indicators. The significant features can be addressed by the added attention mechanism. In the model training process, the rotation speed of the cutter head (N), thrust (F), torque (T), penetration rate (P), and chamber earth pressure (Soil_P) are adopted to predict the advance rate. The influence of the training periods on the model performance is also discussed. The result shows that not only the data amount, but also the data periods have an influence on the prediction. The long-term data may lead to a failure of the advance rate of TBM. The model evaluation result on the test data shows that the proposed model cannot predict the monitored data in the starting stage, which denotes that the working state of TBM in the starting stage is not stable. Especially when the TBM starts to work, the prediction error is big. The proposed model is also compared with several traditional machine methods, and the result shows the excellent performance of the proposed model.

The monitored settlement data can reflect the safety condition of tunnel construction. The monitored settlement data, the experimental results and theoretical calculation outcomes of settlements of the immersed tunnel of Hong Kong-Zhuhai-Macao Bridge (HZMB) were compared and the contributing factors to the settlement and the underlying mechanism of settlement were discussed. The immediate settlement is the major form of settlement of the HZMB tunnel. The immediate settlement accounts for about 70%–80% of the total settlement of the tunnel, which is mainly caused by the gravel cushion and foundation soil. The settlement is 7.2–11.2 mm about the natural foundation section, and that of clay section is larger than sandy soil section. The immediate settlement can be calculated by the formula specified in the Foundation Code of Japan. The results are expected to provide a basis for the design and construction of immersed tunnels in the future.

The large diameter deep inserted steel cylinder is used for the first time in the artificial island project of Hong Kong-Zhuhai-Macao Bridge. Through the ingenious conception of the scheme of large diameter deep inserted steel cylinder, this paper analyzes the working mechanism, design key points and construction key points of large diameter deep inserted steel cylinder structure, and introduces the research and application of large diameter deep inserted steel cylinder technology in foundation pit support engineering of East–West artificial island of Hong Kong Zhuhai Macao Bridge. Two arc-shaped steel plate auxiliary grids are used to connect and seal the adjacent cylinders. The arc steel plate can be manufactured in the factory and transported to the site for construction, which greatly reduces the construction difficulty and material cost. The formed cofferdam Island wall has good water stop performance and good structural stability.

The uplift bearing capacity of pile in soil foundation is mainly obtained by load test, which is time-consuming and low-efficiency. A small sliding model and the contact pairs in large-scale finite element software ABAQUS are used to simulate the friction between pile and soil, and a good fitting curve of pile foundation bearing capacity is obtained. According to the results of finite element software, the lateral friction of the pile is fully developed, and the stress of the pile gradually reaches the ultimate pullup capacity with the increase of pile displacement. The finite element software ABAQUS can be used to simulate uplift capacity of pile.