Buckling failure is one of the classical types of catastrophic landslides developing on inclination-paralleled rock slopes, which is mainly governed by its self-weight, earthquake and ground water. However, nearly none of the existing studies fully consider the influence of slope self-weight, earthquake and ground water on the mechanical model of buckling failure. In this paper, based on energy equilibrium principle and elastoplastic slab theory, a thorough mechanical analysis on bucking slopes has been carried out. Furthermore, an analytical solution for slip bucking failure of rock slopes has been proposed, which fully considers the effect of slope self-weight, seismic force and hydrostatic pressure. Finally, the methodology is used to conduct comparative analysis with other analytical solutions for three practical buckling studies. The results show that the proposed approach is capable of providing a more accurate and reasonable evaluation for stability of rock slopes with potential buckling failure.

This study presents an energy analysis for large-strain cavity expansion problem based on the general strength criterion and energy theory. This study focuses on the energy dissipation problem during the cavity expansion process, dividing the soil mass around the cavity into an elastic region and a plastic region. Assuming compliance with the small deformation theory in the elastic region and the large deformation theory in the plastic region, combined with the general strength criterion of soil mass and energy theory, the energy dissipation solution for cavity expansion problem is derived. Firstly, from an energy perspective, the process of cavity expansion in soil mass is described as an energy conversion process. The energy dissipation mechanism is introduced into the traditional analysis of cavity expansion, and a general analytical solution for cavity expansion related to energy is derived. Subsequently, based on this general analytical solution of cavity expansion, the influence of different strength criterion, large-strain, expansion radius, cavity shape and characteristics of soil mass on the stress distribution, displacement field and energy evolution around the cavity is studied. Finally, the effectiveness and reliability of theoretical solution is verified by comparing the results of typical pressure-expansion curves with existing literature algorithms. The results indicate that different strength criterion have a relatively small impact on the displacement and strain field around the cavity, but a significant impact on the stress distribution and energy evolution around the cavity.

The effective management of damage in tunnels is crucial for ensuring their safety, longevity, and operational efficiency. In this paper, we propose an educational management model tailored specifically for addressing damage in tunnels, utilizing numerical solution techniques. By leveraging advanced computational methods, we aim to develop a comprehensive understanding of the factors contributing to tunnel damage and to establish proactive measures for mitigation and repair. The proposed model integrates principles of tunnel engineering, structural mechanics, and numerical analysis to facilitate a systematic approach to damage assessment, diagnosis, and management. Through the application of numerical solution techniques, such as finite element analysis, we demonstrate the efficacy of the proposed model in simulating various damage scenarios and predicting their impact on tunnel performance. Additionally, the educational component of the model provides valuable insights and training opportunities for tunnel management personnel, empowering them to make informed decisions and implement effective strategies for ensuring the structural integrity and safety of tunnel infrastructure. Overall, the proposed educational management model represents a significant advancement in tunnel management practices, offering a proactive and knowledge-driven approach to addressing damage and enhancing the resilience of tunnel systems.

To stabilize the excavations in urban area, soil anchorage is among the very common methods in geotechnical engineering. A more efficient deformation analysis can potentially lead to cost-effective and safer designs. To this end, a total of 116 three-dimensional (3D) finite element (FE) models of a deep excavation supported by tie-back wall system were analyzed in this study. An initial validation was conducted through examination of the results against the Texas A&M excavation cases. After the validation step, an extensive parametric study was carried out to cover significant design parameters of tie-back wall system in deep excavations. The numerical results indicated that the maximum horizontal displacement values of the wall (δ_hm) and maximum surface settlement (δ_vm) increase by an increase in the value of ground anchors inclination relative to the horizon. Additionally, a change in the wall embedment depth was found to be contributing more to δ_vm than to δ_hm. Based on the 3D FE analysis results, two simple equations are proposed to estimate excavation deformations for different scenarios in which the geometric configuration parameters are taken into account. The model proposed in this study can help the engineers to have a better understanding of the behavior of such systems.

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

Owing to the high strength and abrasive characteristics of cobble-boulders, cutters are easily worn and damaged during shield tunneling, making construction inefficient. In the present work, the stress on the ripper and scraper on the cutterhead was analyzed by the PFC3D-FLAC3D coupling model of shield tunneling to get insight into the performance of the cutterhead for cutting underground cobble and boulders. The numerical calculation results revealed that the increase in trajectory radius leads to a rising stress on the cutters, and the stress on the front cutting surface is greater than that on the back of the cutters. Moreover, the correlation between cutter wear and stress is revealed based on field measurement data. The distribution of the cutter stress is consistent with the cutter wear and breakage characteristics in actual construction, in which more extensive cutter stress is exhibited, extreme cutter wear appears, and more cutter breakage occurs. Finally, the relationship between the cutterhead opening area's layout and cutter wear distribution was investigated, indicating that the cutter wear extent is the most severe in the region where the radial opening ratio dropped sharply.

This study investigates the influence of nano-silica and basalt fiber content, curing duration, and freeze-thaw cycles on the static and dynamic properties of soil specimens. A comprehensive series of tests, including Unconfined Compressive Strength (UCS), static triaxial, and dynamic triaxial tests, were conducted. Additionally, scanning electron microscopy (SEM) analysis was employed to examine the microstructure of treated specimens. Results indicate that a combination of 1% fiber and 10% nano-silica yields optimal soil enhancement. The failure patterns of specimens varied significantly depending on the type of additive. Static triaxial tests revealed a notable reduction in the brittleness index (IB) with the inclusion of basalt fibers. Specimens containing 10% nano-silica and 1% fiber exhibited superior shear strength parameters and UCS. The highest cohesion and friction angle were obtained for treated specimens with 10% nano-silica and 1% fiber, 90 kPa and 37.8°, respectively. Furthermore, an increase in curing time led to a significant increase in UCS values for specimens containing nano-silica. Additionally, the addition of fiber resulted in a decrease in IB, while the addition of nano-silica led to an increase in IB. Increasing nano-silica content in stabilized specimens enhanced shear modulus while decreasing the damping ratio. Freeze-thaw cycles were found to decrease the cohesion of treated specimens based on the results of static triaxial tests. Specimens treated with 10% nano-silica and 1% fiber experienced a reduction in shear modulus and an increase in the damping ratio under freeze-thaw conditions. SEM analysis reveals dense microstructure in nano-silica stabilized specimens, enhanced adhesion of soil particles and fibers, and increased roughness on fiber surfaces.