In geotechnical engineering, the development of efficient and accurate constitutive models for granular soils is crucial. The micromechanical models have gained much attention for their capacity to account for particle-scale interactions and fabric anisotropy, while requiring far less computational resources compared to discrete element method. Various micromechanical models have been proposed in the literature, but none of them have been conclusively shown to agree with the critical state theory given theoretical proof, despite the authors described that their models approximately reach the critical state. This paper modifies the previous CHY micromechanical model that is compatible with the critical state theory based on the assumption that the microscopic force-dilatancy relationship should align with the macroscopic stress-dilatancy relationship. Moreover, under the framework of the CHY model, the fabric anisotropy can be easily considered and the anisotropic critical state can be achieved with the introduction of the fabric evolution law. The model is calibrated using drained and undrained triaxial experiments and the results show that the model reliably replicates the mechanical behaviors of granular materials under both drained and undrained conditions. The compatibility of the model with the critical state theory is verified at both macroscopic and microscopic scales.

Liquefaction resistance and post-liquefaction shear deformation are key aspects of the liquefaction behavior for granular soil. In this study, 3D discrete element method (DEM) is used to conduct undrained cyclic triaxial numerical tests on specimens with diverse initial fabrics and loading history to associate liquefaction resistance and post-liquefaction shear deformation with the fabric of granular material. The influence of several fabric features on liquefaction resistance is first analyzed, including the void ratio, particle orientation fabric anisotropy, contact normal fabric anisotropy, coordination number, and redundancy index. The results indicate that although the void ratio and anisotropy strongly influence liquefaction resistance, the initial coordination number or redundancy index can uniquely determine liquefaction resistance. Regarding post-liquefaction shear deformation, the above quantities do not dictate the shear strain induced after initial liquefaction. Instead, the mean neighboring particle distance (MNPD), a fabric measure previously introduced in 2D and extended to 3D in this study, is the governing factor for post-liquefaction shear. Most importantly, a unique relationship between the initial MNPD and ultimate saturated post-liquefaction shear strain is identified, providing a measurable state parameter for predicting the post-liquefaction shear of sand.

The stress state and density of soil have been considered as the key factors to determine the liquefaction resistance. However, the results of seismic liquefaction case histories, laboratory tests and centrifuge model tests show that the fabric characteristics also influence liquefaction resistance, even more significantly than the contributions of stress state and density. In this study, anisotropic specimens with different consolidation histories were prepared using the 3D Discrete Element Method (DEM) to investigate the influence of fabric characteristics on the mechanical behavior of granular materials and the underlying mechanisms. The simulations revealed that under monotonic shear conditions, horizontally anisotropic specimens exhibited strain hardening and dilatancy characteristics, as well as higher peak strength. Under cyclic shear condition, the normalized liquefaction resistance of the specimens showed a strong linear relationship with the degree of anisotropy, independent of confining pressures and density. Microscopic results indicate that the fabric arrangement aligned with the loading direction leads to the evolution of the mechanical coordination number and average contact force in a manner favorable to resisting loads, which is the underlying mechanism influencing macroscopic mechanical properties. Additionally, the evolution patterns of contact normal magnitude and angle in anisotropic granular materials under cyclic loading conditions were also analyzed. The results of this study provided a new perspective on the macroscopic mechanical properties and the evolution of the microstructure of granular soils under anisotropic conditions.

The macroscopic mechanical properties of granular systems largely depend on the complex mechanical responses of force chains at the mesoscopic level. This study offers an alternative to rapidly identify and predict force chain distributions under different stress states. 100 sets of gradation curves that effectively represent four typical continuous gradation distributions are constructed. Numerical specimens corresponding to these gradation curves are generated using the discrete element method (DEM), and a dataset for deep neural network training is established via biaxial compression numerical simulations. The relationship between particle distribution characteristics and force chain structure is captured by the Pix2Pix conditional generative adversarial network (cGAN). The effectiveness of the generated force chain images in reproducing both particle gradation and spatial distribution characteristics is verified through the extraction and analysis of pixel probability distributions across different color channels, along with the computation of texture feature metrics. In addition, a GoogLeNet-based prediction model is constructed to demonstrate the accuracy with which the generated force chain images characterize the macroscopic mechanical properties of granular assemblies. The results indicate that the Pix2Pix network effectively predicts and identifies force chain distributions at peak stress for different gradation

The study of macroscopic discrete granular materials holds significance in hydraulic engineering, geotechnical engineering, as well as road and bridge engineering. Its foundational scientific exploration bears profound theoretical implications and is of pivotal practical value to engineering endeavors. Within the realm of engineering construction, issues such as dam breakages, earth-rock dam damage, and geological disasters involving loose particles pose substantial threats to the safety of both national livelihoods and property. Thus, delving into the examination of the structural stability of granular materials at the mesoscopic scale becomes an imperative pursuit. In this study, the topological structure of granular materials is identified and segmented based on image processing techniques, and the relationship between the compressive capacity of polygonal structures and the number of polygonal sides is studied. The redundancy function is defined to evaluate the structural stability of granular materials. In addition, the definition of structure tensor is introduced, and redundancy and structure tensor are applied to the study of biaxial compression of shale materials. The research results contribute to improving engineering safety and have guiding merits for the research and application of granular materials. Future work could focus on extending these methods to other types of granular materials and exploring their behavior under different loading conditions. (c) 2025 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.

This paper presents a comprehensive study on the evolution of the small-strain shear modulus (G) of granular materials during hydrostatic compression, conventional triaxial, reduced triaxial, and p-constant triaxial tests using 3D discrete element method. Results from the hydrostatic compression tests indicate that G can be precisely estimated using Hardin's equation and that a linear correlation exists between a stress-normalized G and a function of mechanical coordination number and void ratio. During the triaxial tests, the specimen fabric, which refers to the contact network within the particle assembly, remains almost unchanged within a threshold range of stress ratio (SR). The disparity between measured G and predicted G, as per empirical equations, is less than 10% within this range. However, once this threshold range is exceeded, G experiences a significant SR effect, primarily due to considerable adjustments in the specimen's fabric. The study concludes that fabric information becomes crucial for accurate G prediction when SR threshold is exceeded. A stiffness-stress-fabric relationship spanning a wide range of SR is put forward by incorporating the influences of redistribution of contact forces, effective connectivity of fabric, and fabric anisotropy into the empirical equation.

The cement-stabilization technique is employed on natural and recycled granular materials to improve their mechanical properties. The strength of these materials is assessed by the unconfined compressive strength on laboratory compacted specimens, typically after 7 days of curing. Standards and technical specifications specify different values of specimen height and diameter and different loading modes of testing. This makes the comparison between different materials and with the acceptance limits of technical specifications difficult. The research investigates the effect of specimen size and loading mode on the unconfined compressive strength of both natural and recycled cement-stabilized granular materials. The results revealed significant differences in strength due to variations in specimen size and loading mode. As expected, an increase in specimen slenderness resulted in a decrease in compressive strength. A linear regression model was developed to quantify the effect of the experimental variables on the compressive strength of the two cement-stabilized materials.

Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.

In this paper, the thermodynamics of granular material is developed to get constitutive relations for unified modelling of undrained viscoplastic flow behavior with complex combined effects of state, rate, time, and path. The proposed formulations of energy storages and dissipations lead to the state-dependent hyperelasticity with an elastic instable region and the viscoplasticity with considerations of the granular kinetic flow. Subjected to strict thermodynamic restraints, a generalized law of viscoplastic shear flow is proposed for granular material as the combination of state-based and rate-based viscoplastic flows, which predictively captures the diversity of undrained granular flow pattern with elastic-plastic coupled non-coaxialities among stresses, (total/ viscoplastic/elastic) strains, and their increments. The viscoplastic flow is also linked with the granular temperature that accounts for the granular kinetic fluctuation varying from dilative dense flow to large unlimited flow under shear-induced static liquefaction. This enables predictions of the creep and the stress relaxation as well as the over- and -under shooting of stress under stepwise changes in strain rate. The model is well validated by predicting the flow potential, phase transformation, critical state, and rate/time effects under undrained conventional triaxial shearing and simple shearing for Toyoura sand, which are strongly related to the void ratio, the confining pressure, the shear stress, and the shear mode.

The objective of this study is to develop and evaluate a new type of energy dissipation box damper, designed for use in seismic-resistant structures. This damper utilizes natural granular materials, primarily sand, as the key damping mechanism. The research was conducted through a series of experiments to investigate the mechanical properties of the damper under different operational conditions. The study focused on the effectiveness of sand as a damping agent, the examination of various damper configurations including both single and dual silhouette models, and the assessment of damper performance under variable testing rates. It was found that sand, with its fine and smooth particles, offers excellent damping properties. The single silhouette model, in particular, demonstrated improvements in stability, load distribution, and energy dissipation efficiency when used with sand. The damper showed robust hysteresis behavior, proving its reliability under high-load scenarios. Additionally, the influence of testing rates on the damping behavior was significant, with higher rates improving the damping ratio in sand-based systems. The LuGre model was applied to provide a mathematical representation of the experimental findings; however, some discrepancies were noted, attributed to the model's limitations and the experimental uncertainties. The results highlight the damper's potential as an economical alternative for seismic resistance, leveraging the low cost and simplicity of natural materials. The design of the damper focuses on simplicity and ease of maintenance, making a significant contribution to the progress in earthquake mitigation technologies.