The construction adit plays a pivotal role in enhancing the working face during the excavation of long-distance and deep hydraulic tunnels. However, the intersection zone between the main tunnel and the construction adit exhibits more intricate deformation patterns in surrounding rock, posing a significant threat to stability during excavation. Taking the Xianglushan tunnel in Yunnan Province, China, as a case study, the FLAC3D software is employed to simulate the excavation process at the intersection. The simulation results are verified combined with the field deformation monitoring results, and the spatial distribution of tunnel rock deformation in the intersection area are analyzed. Five excavation conditions with different intersection angles are simulated, and the surrounding rock deformation of the tunnel intersection area with different intersection angles is analyzed, and its influence range is discussed. The results show that: (1) The surrounding rock deformation in the intersection area increases rapidly during the tunnel excavation. With the increase of construction distance, the deformation of intersection area is gradually stable. (2) The deformation distribution of the tunnel rock is uneven, and the deformation of main tunnel near the intersection area is larger than that far away from the intersection area. (3) With the increase of the intersection angle, the surrounding rock deformation of the tunnel intersection and its influence range decreases gradually. The research results have certain guiding significance for the construction safety of the tunnel intersection area.

The rapid development of the economy has compelled the widen of highways, and the main challenge of this undertaking lies in the uneven settlement of road embankments. Through field and numerical experiments, this study explores the deformation mechanism of shallow buried pipelines due to road widening. The utilization of Plaxis3D software, which is adapt at simulating complex engineering geological conditions, enables the simulation of the settlement of both the central and right-side road embankments. Comparing with other numerical software such as ABAQUS and COMSOL, Plaxis provided more constitutive models including HS, HSS and Hoek-Brown model. The work concludes that the uneven settlement of road cross-sections is positively correlated with the horizontal distance from the pipeline, with a maximum settlement of 73 mm observed after construction. Furthermore, based on the Winkler's assumption, theoretical settlement and stress calculation methods are established. Results indicate that the maximum difference between the calculated values of this formula and simulated values is 1.9% and 7%, respectively. Additionally, the study investigates the stress and settlement of the pipeline's top under different angles to understand its behavior under various conditions. It finds that with traffic loads applied to the new embankment, a lever effect occurs on the lower pipeline, with the fulcrum located within the central isolation zone, leading to a transition in curve type from "single peak and single valley" to "double peak and single valley." Moreover, the settlement of pipelines on both sides of the central isolation zone and the normal stress of the pipeline's top section are symmetrical.

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

The weakening effect of water on rocks is one of the main factors inducing deformation and failure in rock engineering. To clarify this weakening effect, immersion tests and post-immersion triaxial compression tests were conducted on sandstone and mudstone. The results showed that the strength of water-immersed sandstone decreases with increasing immersion time, exhibiting an exponential relationship. Similarly, the strength of water-immersed mudstone decreases with increasing environmental humidity, also following an exponential relationship. Subsequently, a statistical damage model for water-weakened rocks was proposed, changes in elastic modulus to describe the weakening effect of water. The model effectively simulated the stress-strain relationships of water-affected sandstone and mudstone under compression. The R² values between the theoretical and experimental peak values ranged from 0.962 to 0.996, and the MAPE values fell between 3.589% and 9.166%, demonstrating the model's effectiveness and reliability. The damage process of water-saturated rocks corresponds to five stages: compaction stage - no damage, elastic stage - minor damage, crack development stage - rapid damage increase, post-peak residual stage - continuous damage increase, and sliding stage - damage completion. This study provides a foundational reference for researching the fracture characteristics of overlying strata during coal mining under complex hydrogeological conditions.

Tunnels offer myriad benefits for modern countries, and understanding their behavior under loads is critical. This paper analyzes and evaluates the damage to buried horseshoe tunnels under soil pressure and traffic loading. To achieve this, a numerical model of this type of tunnel is first created using ABAQUS software. Then, fracture mechanics theory is applied to investigate the fracture and damage of the horseshoe tunnel. The numerical analysis is based on the damage plasticity model of concrete, which describes the inelastic behavior of concrete in tension and compression. In addition, the reinforcing steel is modeled using the bilinear plasticity model. Damage contours, stress contours, and maximum displacements illustrate how and where traffic loading alters the response of the horseshoe tunnel. Based on the results, the fracture mechanism proceeded as follows: initially, damage started at the center of the tunnel bottom, followed by the formation of damage and micro-cracks at the corners of the tunnel. Eventually, the damage reached the top of the concrete arch with increasing loading. Therefore, in the design of this tunnel, these critical areas should be reinforced more to prevent cracking.

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.

The settlement, bearing capacity, axial force, and skin friction responses of pervious and impervious concrete piles in silty and sandy underlying layer foundations and of pervious concrete piles in model tests were determined. The results showed that pervious concrete piles can exhibit high strengths, provide drainage paths and thus reduce foundation consolidation time. Increasing the soil layer thickness and pile length could eliminate the bearing capacity difference of pervious piles in a foundation with a silty underlying layer. The pervious concrete piles in the sandy underlying layer were more efficacious than those in the silty underlying layer because the sandy underlying layer can provide more bearing capacity than the silty underlying layer. The results indicated that the performances of the pervious concrete piles in the sand and silt foundations differed. The pervious concrete piles functioned as floating piles in the underlying layer with a lower bearing capacity and as end-bearing piles in the underlying layer with a higher bearing capacity.

The present paper investigates the spatial variability effect of soil property on the three-dimensional probabilistic characteristics of the bearing capacity factor (i.e., mean and coefficient of variation) of a circular footing resting on clayey soil where both mean and standard deviation of undrained shear strength increases with depth, keeping the coefficient of variation constant. The mean trend of undrained shear strength is defined by introducing the dimensionless strength gradient parameter. The finite difference method along with the random field and Monte Carlo simulation technique, is used to execute the numerical analyses. The lognormal distribution is chosen to generate random fields of the undrained shear strength. In the study, the potential failure of the structure is represented through the failure probability. The influences of different vertical scales of fluctuation, dimensionless strength gradient parameters, and coefficient of variation of undrained shear strength on the probabilistic characteristics of the bearing capacity factor and failure probability of the footing, along with the probability and cumulative density functions, are explored in this study. The variations of failure probability for different factors of safety corresponding to different parameters are also illustrated. The results are presented in non-dimensional form as they might be helpful to the practicing engineers dealing with this type of problem.