International Journal for Numerical and Analytical Methods in Geomechanics最新文献

Plasticity and fracture problems have always been hot topics in numerical methods. In this work, a universal implementation procedure for the elasto-plastic constitutive model is developed in the four-dimensional lattice spring model (4D-LSM), in which the Jaumann stress rate is incorporated to exclude the influence of the rigid rotation in the particle stress, expanding the ability of 4D-LSM to deal with large elastic deformation problems by its own to large plastic deformation problems. As an example, the Zienkiewicz–Pande (ZP) constitutive model is implemented. Several numerical examples are carried out to check the performance of the implemented model. Through a comparison with analytical solutions, available experimental data, and other numerical results, the stability of the developed plastic framework and the correctness of the stress calculation scheme are verified. Meanwhile, numerical results show that the developed code is capable of solving elasto-plastic large deformation problems. With the advantage of 4D-LSM in handling fracture problems, the ability of the embedded model to solve plastic fracture problems is verified with a simple maximum deformation failure criterion.

The ice–water phase transition of bulk ice could develop with varying temperatures and external loads, significantly affecting its mechanical properties. The coupling effect of temperature and shear loads on the thermo-mechanical properties of bulk ice and its phase transition evolution is poorly understood, especially at the nanoscale. In this study, molecular dynamics (MD) simulation method was employed to investigate the thermo-mechanical behaviours of bulk ice-Ih system at the microscale under various temperatures (73–270 K) and shear paths, where its phase transition, elastic properties, structure deformation mechanism and structural anisotropy were discussed. The simulation results show that (1) the shear modulus, shear strength and ultimate shear strain of bulk ice-Ih system could linearly decrease with rising temperature, aligning with previous studies. (2) Two types of failure modes from bulk ice-Ih system were founded, such as solid–liquid phase co-existence at 73–225 K and liquid phase at 250–270 K. (3) Ice melting into water was attributed to the fracture of hydrogen bond during shear process. (4) Compared to vertical shearing (XZ ($��11�\overline{2}�0�$) and YZ ($��01�\overline{1}�0�$)) directions, the mechanical response along the horizontal shearing (XY (0001)) direction was most sensitive to temperature effect.

Existing solutions for electro-osmotic consolidation assume a linear voltage distribution, which is inconsistent with the experimental findings. The present study introduces a novel two-dimensional electro-osmotic consolidation model for unsaturated soils, which considers the influence of non-linear voltage distribution. The closed-form solution is derived by employing the eigenfunction expansion method and the Laplace transform technique. The accuracy of the analytical solutions is validated through the implementation of finite element simulations. The findings from the parametric studies indicate that the excess pore water pressure (EPWP) observed in electro-osmotic consolidation is influenced by the distribution of voltage. The dissipation rate of EPWP is observed to be higher when subjected to non-linear voltage conditions compared to linear voltage conditions. Moreover, the impact of non-linear voltage distribution becomes more pronounced in unsaturated soil characterised by higher electro-osmosis conductivity and a lower ratio of k_x/k_y. In contrast, the excess pore air pressure (EPAP) remains unaffected by the voltage distribution.

An analytical solution based on the infinite layer theory of Novak and Biot's consolidation equation is developed in this study to evaluate the impact of local debonding occurring at the pile–soil interface. The potential functions are employed to decouple the differential equations that govern the soil deformations, while the dynamic resistances of soil are determined from the boundary conditions at the pile–soil interface in accordance with computational theory for mixed boundary problems. The Adomian decomposition method is introduced to obtain the dynamic impedances of pile. The effects of local debonding on the dynamic resistances of soil are investigated by comparing the results from the present solution with available schemes based on perfect contact assumption. The influences of pile–soil modulus ratio, exciting frequency, soil permeability, and slenderness ratio of pile while considering local debonding were then examined. The numerical results indicate that the local debonding occurring at the pile–soil interface dramatically weakened the lateral dynamic impedances of pile, and this trend was particularly pronounced at high frequency and small modulus ratio. Additionally, the local debonding phenomenon also imposes limitations on the implementation of the equivalent single-phase solution in practical engineering applications. The presented solution theoretically demonstrates the significant impact of local debonding on the dynamic response of piles embedded in saturated soil and may provide insight into determining parameter values in empirical equations.

Landfills emissions, ranking as the third-largest anthropogenic source of methane in the atmosphere, pose environmental challenges and threaten public health. The pivotal role of clay as a mitigating agent for methane emission within landfill cover systems cannot be overstated; however, our understanding of methane escape from fractured clay remains limited. This study aims to address the existing gaps by proposing a robust analytical model of methane transport in both fractures and clay matrix. Our investigation also includes a dimensionless analysis to govern the relative significance of diffusion and advection in methane emission from fractured clay, systematically reviewing factors such as the degree of water saturation (Sr) and fracture width. The methane concentration profiles in cracked clay demonstrated escalating sensitivity to Péclet (Pe) numbers, especially when advection dominates transport. Our findings also highlight the prevalence of preferential methane flow with increasing Sr in the clay matrix. The flux of methane emission from fractures at Sr = 0.8 was 130 times greater than that from intact clay. However, the study necessitates considering methane emission from clay matrix, particularly in dry clay conditions (Sr = 0.2 and 0.4). The accumulated methane emission flux from intact clay, more than that emitted from fractures by about 2.5 times at Sr = 0.2, was 1.3 × 10⁻⁵ g/m/s. The findings significantly advance the understanding of gas transport in fractured geomaterials, revealing the effect of water saturation and crack width on methane emissions from fractures. Overall, the outcomes emphasize the inclusion importance of methane emission from cracked clay in the design of gas barriers.

The long-core SDCM pile is a typical type of stiffened deep cement mixing (SDCM) pile, it could be widely exploited in coastal geotechnical engineering because of its high bearing capacity, low settlement, green, and economic advantages. The long-core SDCM pile is constituted by a PHC pipe pile and cemented soil, the height of the PHC pipe pile is upward than the depth of the cemented soil reinforcement. This study implements a theoretical approach to load transfer analysis of the long-core SDCM pile under vertical load in layer soil. Herein, the shear constitutive models of the DCM pile-PHC pipe pile interface and the fictitious soil pile-PHC pipe pile interface are double exponential models, the compression constitutive model of the soil under the pile and the shear constitutive models of the DCM pile–soil interface and the fictitious soil pile–soil interface are ideal elastic–plastic models. The results obtained from this calculation model can match well with the data from on-site tests and other analytical solutions. The theoretical model is used to analyze the key parameters L_D/L_P, D_D/D_P, E_c, and E_p of the long-core SDCM pile. The L_D/L_P and D_D/D_P are the critical parameters affecting the bearing characteristics, and the minor settlement is affected by the changes of E_c and E_p.

Among all the factors affecting the destructiveness of debris flow, the mean velocity is one of the most important characteristics. In this paper, we aim to apply a particle swarm optimization (PSO) based on the relevance vector machine (RVM) to predict the mean velocity. The PSO is used to optimize kernel parameters inside the RVM, whereas the RVM is responsible for completing the prediction task. Through sample training, a nonlinear relationship can be obtained, enabling a rapid prediction of the mean velocity for new samples. The debris flow dataset of Jiangjia Gully is used to evaluate the performance of PSO-RVM in this study. Besides, we further compare the prediction results of PSO-RVM with other prominent approaches, for example, the support vector machine (SVM), BP neural network (BP), and the RVM. The results show that the mean relative error (MRE) of PSO-RVM is only 0.69%. In addition, BP yields the highest MRE (27.61%), and the MRE (2.75%) corresponding to the RVM is lower than that (5.98%) yielded by the SVM. For the root mean square error (RMSE) and Theil's inequality coefficient (TIC), the PSO-RVM method still generates much lower RMSE (6.48%) and TIC (0.179%) values than the other three methods. Overall, compared with current debris flow prediction models, the PSO-RVM achieves high prediction accuracy, fewer optimization parameters, and low computational complexity. Finally, a sensitivity analysis is conducted to explore the dominative factors of debris flow.

This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (M_DD) and optimum moisture content (O_MC), which are critical for stabilizing construction foundations. Traditional methods for determining M_DD and O_MC are labor-intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF-R), gradient boosting regression (GB-R), XGBoosting regressor (XGB-R), and multilinear regression (ML-R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB-R demonstrated the highest prediction accuracy for M_DD and O_MC, outperforming RF-R, XGB-R, and ML-R. The performance of built-in models has been measured by three new index performance metrics: the a20-index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that M_DD and O_MC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve-fitting techniques were applied to model the relationship between M_DD, O_MC, and these key factors. The results indicated that the GB-R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.