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The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model's receptive field, and MDConv is introduced to enhance the model's feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model. [ABSTRACT FROM AUTHOR]

In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly used to calculate structural deformation, simplifying the interaction between segments and soil as radial and tangential Winkler springs. However, when introducing connection springs or reduction factors to simulate the joint stiffness of segments, the challenge lies in determining the reduction coefficient and the stiffness of the springs. Currently, the hyperstatic reflection method cannot simulate the discontinuity effect at the connection of the tunnel segments, while the state space method overlooks the nonlinear interaction between the tunnel and the soil. Therefore, this paper proposes a numerical simulation method considering the interaction between the tunnel and the soil, which is subjected to compression rather than tension, and the discontinuity of the joints between the segments. The model structure and external load are symmetrical, resulting in symmetrical calculation results. This method is based on the soft soil layers and shield tunnel structures of the Shanghai Metro, and the applicability of the model is verified through deformation calculations using three-dimensional laser scanning point clouds of sections from the Shanghai Metro Line 5. When the subgrade reaction coefficient is 5000 k N / m 3 , the model can effectively simulate the deformation of operational tunnels. By adjusting the bending stiffness of individual connection springs, we investigate the influence of bending stiffness reduction on the bending moment, radial displacement, and rotational displacement of the ring. The results indicate that a decrease in joint bending stiffness significantly affects the mechanical response of the ring, and the extent and degree of this influence are correlated with the joint position and the magnitude of joint bending stiffness. [ABSTRACT FROM AUTHOR]

The transfer learning method, based on unsupervised domain adaptation (UDA), has been broadly utilized in research on fault diagnosis under variable working conditions with certain results. However, traditional UDA methods pay more attention to extracting information for the class labels and domain labels of data, ignoring the influence of data structure information on the extracted features. Therefore, we propose a domain-adversarial multi-graph convolutional network (DAMGCN) for UDA. A multi-graph convolutional network (MGCN), integrating three graph convolutional layers (multi-receptive field graph convolutional (MRFConv) layer, local extreme value convolutional (LEConv) layer, and graph attention convolutional (GATConv) layer) was used to mine data structure information. The domain discriminators and classifiers were utilized to model domain labels and class labels, respectively, and align the data structure differences through the correlation alignment (CORAL) index. The classification and feature extraction ability of the DAMGCN was significantly enhanced compared with other UDA algorithms by two example validation results, which can effectively achieve rolling bearing cross-domain fault diagnosis. [ABSTRACT FROM AUTHOR]

With the rapid development of the high-speed railway industry, the safety of railway operations is becoming increasingly important. As a symmetrical structure, traditional manual patrol and camera surveillance solutions on both sides of the railway require enormous manpower and material resources and are highly susceptible to weather and electromagnetic interference. In contrast, a distributed fiber optic vibration sensing system can be continuously monitored and is not affected by electromagnetic interference to false alarms. However, it is still a challenge to identify the type of intrusion event along the fiber optic cable. In this paper, a railway intrusion event classification and location scheme based on a distributed vibration sensing system was proposed. In order to improve the accuracy and reliability of the recognition, a 1 DSE-ResNeXt+SVM method was demonstrated. Squeeze-and-excitation blocks with attention mechanisms increased the classification ability by sifting through feature information without being influenced by non-critical information, while a support vector machine classifier can further improve the classification accuracy. The method achieved an accuracy of 96.0% for the identification of railway intrusion events with the field experiments. It illustrates that the proposed scheme can significantly improve the safety of railway operations and reduce the loss of personnel and property safety. [ABSTRACT FROM AUTHOR]

Extracting complete cross-sectional geometric features from the large amount of point cloud data acquired by laser scanners plays an important part in the detection of deformations in tunnel inspection projects. Tunnel cross-sections have symmetrical geometric features, and information is traditionally collected manually. The traditional manual extraction of point clouds is inefficient and limited by the subjectivity of the operators when addressing the problems. This paper proposes a new algorithm for the automatic identification of tunnel lining section curves, the rapid separation of common interference targets, and the optimization of curve geometry features. The innovation of this approach lies in the combination of B-spline and Euclidean clustering methods and the comprehensive evaluation of the denoising results in terms of precision, recall, F-score, and rand index (RI). In this way, the automatically extracted health point cloud data are refitted to optimize the tunnel profile model. [ABSTRACT FROM AUTHOR]

With the continuous development of engineering construction in China, more and more large-section highway tunnels have emerged. Different geological engineering environments determine the diversity of construction plans. The determination of construction plans and the prediction of tunnel deformations have always been the key points of engineering construction. In this paper, we use numerical simulations to determine specific construction parameters in the context of actual highway tunnel projects, and then use deep learning methods to predict deformation during tunnel construction, thus providing guidance for construction. We have found that: (i) Different excavation sequences and excavation depths have different effects on the surrounding rock deformation around the tunnel. The optimal excavation sequence through numerical simulation in this study is symmetrical excavation, and the excavation depth is 2 m. (ii) Numerical simulation based on Long Short-Term Memory (LSTM) algorithm is used to predict the tunnel deformation. It is found that the prediction results of the LSTM algorithm are more consistent with the actual monitoring data. (iii) Multi-step prediction is more important for engineering guidance, and three-step prediction can be considered during the process of engineering construction. Therefore, the machine learning algorithm provides a new method for engineering prediction. [ABSTRACT FROM AUTHOR]

The research on the deformation of soil mass and ground connection walls is not sufficiently thorough due to the huge risk of deep excavation in soft soil areas. In this paper, finite element software is used to numerically simulate a symmetrical foundation pit in Suzhou, and the reliability is verified by on-site measured data. The purpose of this study is to investigate the deformation mechanism of the enclosure structure and surrounding soil during the excavation of soft soil foundation pits, and to carry out sensitivity analysis. The results show that the maximum subsidence of the surface is 21.25 mm, the maximum horizontal displacement of the underground diaphragm wall is 9.45 mm, and the maximum uplift of the pit bottom is 21.46 mm. By changing the soil layer properties (the elastic modulus, cohesion, and internal friction angle) and the insertion ratio of the diaphragm wall, the maximum horizontal displacement of the diaphragm wall is more easily affected than the maximum settlement of the surface. Based on different research results, the maximum land subsidence and maximum horizontal displacement of the support structure are 0.313–0.060% and 0.070–0.250% of the maximum excavation depth, respectively. Finally, the simulation may have some applicability to other foundation pit excavations. [ABSTRACT FROM AUTHOR]

The rectangular utility tunnels have been increasingly built in urban areas in recent decades. In this paper, a case of jacked box tunnels built closely over-crossing the existing subway tunnels in Suzhou, China, is presented. The tunneling-induced deformation of the subway tunnels is measured and analyzed. The measurement indicates that the subway tunnels are uplifted and subjected to the overcrossing box tunneling. As compared, the longitudinal deformation of the range that locates under the box tunnels is more significant than that outside the range. The tunneling-induced deformation of the left subway tunnel is relatively higher than that of the right one. The numerical simulation is performed for validation. The numerical result is agreed with the in-situ measurements. Moreover, the simulation reveals that the presence of friction in jacked tunnels causes the extra uplift deformation of subway tunnels. The higher the friction, the is the deformation. It is affirmed that using the effective lubrication not only facilitates the tunnel jacking but also inabilities the tunneling-induced deformation of underlying tunnels [ABSTRACT FROM AUTHOR]