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.

Water surface garbage has a significant impact on the protection of water environments and ecological balance, making water surface garbage object detection a critical task. Traditional supervised object detection methods require a large amount of annotated data. To address this issue, we propose a method that combines strong and weak supervision with CLIP (Contrastive Language–Image Pretraining) for water surface garbage object detection. First, we train on a dataset annotated with strong supervision, using traditional object detection algorithms to learn the location information of water surface garbage. Then, we input the water surface garbage images into CLIP’s visual encoder to obtain visual feature representations. Simultaneously, we train CLIP’s text encoder using textual description annotations to obtain textual feature representations of the images. By fusing the visual and textual features, we obtain comprehensive feature representations. In the weak supervision training phase, we input the comprehensive feature representations into the object detection model and employ a training strategy that combines strong and weak supervision to detect and localize water surface garbage. To further improve the model’s performance, we introduce attention mechanisms and data augmentation techniques to enhance the model’s focus and robustness towards water surface garbage. By conducting experiments on two water surface garbage datasets, we validate the effectiveness of the proposed method based on the combination of strong and weak supervision with CLIP for water surface garbage object detection tasks.

Concrete dam deformation prediction plays a crucial role in ensuring dam safety monitoring. Machine learning, especially recurrent neural networks, plays an important role in this, yet its predictive accuracy is not high. This paper first preprocesses time series data using successive variational mode decomposition (SVMD). Then, it combines residual network (ResNet) and gate recurrent unit (GRU), fully leveraging the excellent information extraction capability of the former and the powerful sequential data processing capability of the latter, proposing the ResNet‐GRU model for deformation prediction analysis. During the prediction process, the sanitized gray wolf optimizer (SGWO) algorithm adjusts the parameters, further enhancing the prediction accuracy and performance of the model. The model has also been successfully applied in practical cases, validating its effectiveness. The results indicate that the ResNet‐GRU‐SGWO model can accurately simulate the dynamic deformation process of concrete dams. Compared with various intelligent prediction models, it achieves the highest prediction accuracy and outstanding performance, providing a reference for the prediction of concrete dam deformation.