Safety assessment of ductile iron (DI) pipelines under fault rupture is a crucial aspect for underground pipeline design. Previous studies delved into the response of DI pipelines to strike-slip faults, but all existing theoretical methods for DI pipelines under strike-slip faults are not suitable for normal fault conditions due to the difference in soil resistance distribution. In this study, analytical solutions considering asymmetric soil resistance and pipe deflection are developed to analyze the behavior of DI pipelines under normal faulting. Results indicate that DI pipelines with a longer segment length are more vulnerable to pipe bending damage, while exhibiting a lower sensitivity to joint rotation failure. For the conditions of pipe segment length L = 1.5 m at all burial depths and L = 3 m at a shallow burial depth, when the fault-pipe crossing position shifts from a joint to a quarter of the segment length (rp = 0 similar to 0.25), DI pipelines are more prone to joint rotation failure. However, in the cases of L = 3 m at a moderate to deep burial depth and L = 6 m at all burial depths, the most unfavorable position is rp = 0.75, dominated by the mode of pipe bending failure.

Shield tunnels in operation are often affected by complex geological conditions, environmental factors, and structural aging, leading to cumulative damage in the segments and, consequently, increased deformation that compromises structural safety. To investigate the deformation behavior of tunnel linings under random damage conditions, this study integrates finite element numerical simulation with deep learning techniques to analyze and predict the deformation of shield tunnel segments. First, a refined three-dimensional finite element model was established, and a random damage modeling method was developed to simulate the deformation evolution of tunnel segments under different damage ratios. Additionally, a statistical analysis was conducted to assess the uncertainty in deformation caused by random damage. Furthermore, this study introduces a convolutional neural network (CNN) surrogate model to enable the rapid prediction of shield tunnel deformation under random damage conditions. The results indicate that as the damage ratio increases, both the mean deformation and its variability progressively rise, leading to increased deformation instability, demonstrating the cumulative effect of damage on segment deformation. Moreover, the 1D-CNN surrogate model was trained using finite element computation results, and predictions on the test dataset showed excellent agreement with FEM calculations. The surrogate model achieved a correlation coefficient (R2) exceeding 0.95 and an RMSE below 0.016 mm, confirming its ability to accurately predict the deformation of tunnel segments across different damage conditions. To the best of our knowledge, the finite-element-deep-learning hybrid approach proposed in this study provides a valuable theoretical foundation for predicting the deformation of in-service shield tunnels and assessing structural safety, offering scientific guidance for tunnel safety evaluation and damage repair strategies.

The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation.

The ground penetrating shield tunnel (GPST) method offers a streamlined approach to tunnel construction in soft ground with limited open-cut excavation. To explore the seismic response of GPST linings, a series of large-scale shaking table tests have been conducted, including a variety of seismic excitations. This paper focuses on lateral harmonic excitation. The model tunnel spans a total length of 7.7 m, with the embedment depth ranging from -0.5 to 0.5 times its diameter. The design and fabrication of the model tunnel are presented, including the segmental lining, along with circumferential and longitudinal joints. The soil was modeled with artificial synthetic soil, aiming to simulate the static and dynamic characteristics of the prototype soil. Its composition was adjusted and verified through element tests. The experimental results provide insights into the seismic response of the soil-tunnel system, the ovaling deformation of the segmental lining, as well as the response of the joints between lining segments. The results reveal a strong influence of embedment on tunnel seismic response. The reduction of tunnel embedment leads to a significant increase in lining accelerations and a phase difference, resulting in a whiplash effect. In contrast, the ovaling deformation of the lining and the joint apertures decrease with the reduction of embedment. In the sections of the tunnel that are fully embedded, both the acceleration and deformation response of the lining are governed by soil-structure interaction (SSI). A pronounced whiplash effect is observed in the sections of the tunnel that are not fully embedded, due to the absence of soil confinement. The presented experimental results offer valuable insights into the seismic response of GPSTs, which can be of crucial importance for their seismic design.

With the rapid advancement of rail transit, shield tunnels have been extensively constructed worldwide. However, leakage at the shield tail can lead to severe consequences, including shield machine subsidence, structural damage to the tunnel, or even catastrophic tunnel collapse. Research on tunnel collapse induced by shield tail leakage remains in its infancy. The mechanisms underlying such accidents are not yet fully understood by researchers and engineers, and effective preventive measures have yet to be developed. In this study, a reducedscale model test was conducted to investigate the processes and mechanisms of tunnel collapse induced by shield tail leakage. The findings reveal that tunnel collapse is primarily triggered by the impact loads generated from the destabilized soil cave. The soil cave, formed due to erosion caused by leakage, propagates upward in a cycle of destabilization and regeneration until the ground surface collapses, resulting in load redistribution around the tunnel. Additionally, the study compares tunnel collapses induced by shield tail leakage and connecting passage leakage, highlighting that while both share similar collapse mechanisms, their boundary conditions differ. The coupling effect between the tunnel structure and surrounding soil is more pronounced in shield tail leakage, leading to more intense load fluctuations and greater structural damage to the tunnel.

Occlusions of granular particles in images significantly affect the accuracy of evaluating particle morphology for granular materials. In this study, a novel framework of SOLO-PCNet is proposed, which can automatically segment all the particles and predict the complete contours of the occluded particles in densely packed materials. Firstly, the instance segmentation model SOLOv2 is trained for the prediction of all the detectable particles. Then a self-supervised learning algorithm PCNetM is introduced for the inference of the complete contours of the occluded particles so that the prediction of SOLOv2 can be directly input to PCNet-M for the subsequent completion. Thereafter, the particle morphology characteristics including elongation, equivalent mean size, convexity, and circularity are automatically calculated. Then, the evaluation metrics of the segmentation model and morphology characteristics are validated, and the results exhibit the strong generalization ability of the segmentation and completion tasks. Finally, the uncertainty of the completed contours with morphology properties is explored for reliable analysis. This study successfully acquires the complete contours for each particle and provides the foundation for evaluating the mechanical properties of the packed granular materials from individual particles.

When subjected to external loads from the ground and nearby construction, tunnel segmental lining joints are prone to damaging deformation. This can result in water leakage into tunnels, posing great safety risks. With this issue in mind, we conducted a series of full-scale tests to study the effects of external loads on the waterproofing performance of longitudinal joints. A customized rig for testing segmental joints was developed to assess the effect of loading magnitude, eccentricity, and loading-unloading-reloading cycles on waterproofing performance. Additionally, the relationship between joint force, sealing gasket deformation, and waterproofing pressure was investigated. The results indicate that: (1) the sealing gasket's compression rapidly decreases as external loads increase, which weakens the waterproofing capacity of the joint; (2) the watertightness limit dramatically decreases as the bending moment increases; (3) a loading-unloading-reloading cycle leads to degradation of the joint' s waterproofing performance. The findings of this study provide a reference for subsequent waterproofing design of segmental tunnel joints, helping ensure the safety of tunnels throughout their operational lifespans.

In this paper, a seismic and vibration reduction measure of subway station is developed by setting a segmented isolation layer between the sidewall of structure and the diaphragm wall. The segmented isolation layer consists of a rigid layer and a flexible layer. The rigid layer is installed at the joint between the structural sidewall and slab, and the flexible layer is installed at the remaining sections. A diaphragm wall-segmented isolation layer-subway station structure system is constructed. Seismic and vibration control performance of the diaphragm wall-segmented isolation layer-subway station structure system is evaluated by the detailed numerical analysis. Firstly, a three-dimensional nonlinear time-history analysis is carried out to study the seismic response of the station structure by considering the effect of different earthquake motions and stiffness of segmented isolation layer. Subsequently, the vibration response of site under training loading is also studied by considering the influence of different train velocities and stiffness of the segmented isolation layer. Numerical results demonstrate that the diaphragm wall-segmented isolation layer-subway station structure system can not only effectively reduce the lateral deformation of station structure, but also reduce the tensile damage of the roof slab. On the other hand, the developed reduction measure can also significantly reduce the vertical peak displacements of site under training loading.

The position of seal roof block may greatly affect the overall performance of the segmental tunnel. However, a few investigations have been dedicated to evaluating this effect. In the present study, finite element models are established to simulate the ring structure-soil model in order to evaluate the capacity curve of the universal ring structure during progressive failure. Nineteen universal ring structures configurations were analyzed considering different locations of the adjacent seal roof blocks. The models accurately simulated the structural details of the typical handhole for curved bolt in the circumferential and longitudinal joints. The seismic performance of the universal ring structure with different configurations is evaluated and discussed in terms of the moment curve, plastic zone distribution, joint opening angle curve and capacity curve of the universal ring. The results indicate that there are significant differences in the maximum positive and negative moment, plastic zone, and tensile angle of the universal ring structure when the seal roof block is in different positions. At the same time, the development of the performance curve of the ring will also show significant differences, especially in the elastic stage. The structure exhibits best seismic performance when the seal roof block is located at arch shoulder and waist (- 67.5 degrees +/- 22.5 degrees) because it is less likely to reach the normal function, immediate operational, rectifiable or irreparable damage stages compared to other configurations.