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Modeling of the soil–structure interface has been a critical issue in geotechnical engineering. Numerous studies have simulated complex soil–structure interface behaviors. These models usually are assessed by direct comparisons between the simulations and experiments. However, little work has been done to compare the specific interface behaviors simulated by different interface models. This paper evaluated some frequently recognized interface behaviors for six different interface models. These models either were adopted from the existing literature or modified from the existing soil models, including the exponential model, hyperbolic model, hypoplastic model, MCC model, SANISAND model, and SIMSAND model. Global comparisons and effects of the soil density, normal stiffness, and shearing rate were investigated to evaluate the interface models based on Fontainebleau sand–steel interface experiments and kaolin clay–steel interface experiments. The limitations and advantages of different models under different conditions were discussed. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic (COVID-19) has led to a surge in disposable mask waste, posing an urgent environmental challenge. This study explores a sustainable solution by using mask chips as reinforcement material in geotechnical engineering, simultaneously improving ground strength and recycling waste. The mechanical behavior of Fujian sand reinforced with mask chips under plane strain biaxial shear condition was examined. Biaxial shear tests were performed on dense pure sand and sand samples containing three mask chip sizes (20 mm × 5 mm, 10 mm × 10 mm, and 12 mm × 3 mm) at a 0.5% mass ratio, under effective lateral pressures of 50 and 100 kPa. The test results show that incorporating mask chips improved strength and delayed stress peak, with the 20 mm × 5 mm chips demonstrating the best performance. Within the tested lateral pressure range, the sample dilates along the minor principal strain direction during shearing. The volumetric strain of mask–sand mixtures exhibits more significant contraction before the dilation compared with pure sand. Moreover, two distinct shear band shapes were observed through imaging technique where the thickness of the shear band decreases as the lateral pressure rises. Additionally, the Roscoe solution was found to provide the closest approximation of the inclination angle of the shear band in mask-reinforced sand. [ABSTRACT FROM AUTHOR]

The structured sand presents significant interparticle bonding and anisotropy, resulting in significant differences in the physical and mechanical properties from the pure sand. This study proposes a new surrogate model based on the concept of multi‐fidelity residual neural network (MR‐NN) as an alternative to DEM simulation for predicting the mechanical behaviours of structured sand with different initial anisotropy and saving largely computational costs. The model is initially trained using low‐fidelity (LF) data to focus on capturing the main underpinning correlations between macroscopic mechanical parameters with inter‐particle properties and anisotropic state variables (i.e., microscopic fabric and tilting angle of sand), where the LF data are generated from previously proposed anisotropic macro‐micro quantitative correlation. Subsequent training on sparser high‐fidelity (HF) data is used to calibrate and refine the model with HF data generated from DEM simulations for anisotropic structured sand. Feedforward neural network (FNN) is adopted as the baseline algorithm for training models. The macroscopic anisotropic parameters of structured sand predicted by the surrogate model are compared with DEM simulations to examine its feasibility and generalization ability. Furthermore, the robustness of the surrogate model is examined by discussing the effect of LF data on the performance of MR‐NN. The superiority of the MR‐NN is further verified by comparing the performance of the trained MR‐NN with the one‐shot trained FNN based on the same HF data. All results demonstrate that the proposed surrogate model can provide a fast and accurate simulation of the anisotropic parameters of structured sand. [ABSTRACT FROM AUTHOR]

Previous plasticity models for predicting anchor kinematics under eccentric loading in clay are mostly rigid plastic. They may produce inaccurate outcomes in some conditions. This paper presents an elastoplastic model to address this issue. Closed-form solutions for anchor velocities are derived considering two types of boundary conditions: the padeye (1) fastened to a mooring chain, with the chain behavior modeled; or (2) hinged to a rigid rod. Compared with previous methods, the proposed method more realistically captures the soil behavior while avoiding complicated iterative procedures for solving anchor velocities, thus enabling anchor trajectories to be accurately and efficiently obtained by simple time integration. A series of 3D large deformation finite element analyses are conducted to study the anchor behaviors under different eccentric loading, and the proposed model is used to reproduce these simulations. Their results are compared for validation, with good agreements achieved. These results indicate that anchors under eccentric loading will ultimately translate along a fixed direction with constant soil reaction forces. Analytical solutions for this ultimate state are given based on the proposed method. Particularly, the ultimate resultant soil reaction force is demonstrated always passing through the anchor center. This is an assumption in a previous literature but is found to be a consequence of the proposed anchor constitutive relations. [ABSTRACT FROM AUTHOR]

Identifying governing equations from data and solving them to acquire spatio-temporal responses is desirable, yet highly challenging, for many practical problems. Data-driven modelling has shown significant potential to influence knowledge discovery in complex geotechnical processes. To demonstrate feasibility, in this study a physics-informed data-driven approach is developed to automatically recover Terzaghi's consolidation theory from measured data and obtain the corresponding solutions. This process incorporates several algorithms including sparse regression and prior information-based neural network (PiNet), transformed weak-form partial differential equations (PDEs) (to reduce sensitivity to noisy measurement) and Monte Carlo dropout to achieve a measure of prediction uncertainty. The results indicate that consolidation PDEs can be accurately extracted using the proposed approach, which is also shown to be robust to noisy measurements. PDEs solved by PiNet are also shown to provide excellent agreement with actual results, thus highlighting its potential for inverse analysis. The proposed approach is generic and provides an auxiliary method to verify heuristic interpretations of data or to directly identify patterns and obtain solutions without the need for expert intervention. [ABSTRACT FROM AUTHOR]

Vertical drain assisted by vacuum and/or surcharge preloading is an effective method for improvement of soft ground with high water content. A large settlement will occur, and the water flow may deviate from the Darcy's law. The creep is also non-negligible in estimating the long-term settlement of such soft ground. To accurately predict the consolidation process, this study develops an axisymmetric finite strain consolidation model based on Barron's free-strain theory incorporating the creep, radial and vertical flows, non-Darcian flow law, and void ratio-dependent hydraulic conductivity during the consolidation process. First, to mathematically validate the model and highlight the new model's features, the existing model not considering the creep and the non-Darcy flow is also adopted as a reference for comparison based on a benchmark simulation. Then, Rowe cell tests involving non-Darcian flow are simulated by the new model to experimentally validate the predictive performance. Furthermore, the model is applied to simulate the consolidation process of a long-term monitoring embankment to examine the applicability of the model for engineering practice. All results demonstrate that the model is capable of accurately describing the consolidation of soft soils with vertical drains under combined loading with features of creep and non-Darcy flow. • An axisymmetric finite strain consolidation model is developed, incorporating the extended 1D Yin–Graham EVP model and a non-Darcy flow law. • Consolidation governing equations including the radial and vertical water flow are solved using alternating direction implicit (ADI) method. • The model performance describing non-Darcian flow and creep is verified by comparing with physical model tests and long-term field test. [ABSTRACT FROM AUTHOR]

The increasing use of rock‐socketed piles highlights the importance of developing a suitable design method for their bearing capacity. This study quantifies the shear behavior of the rock–pile interface, which generally dominates the bearing capacity of rock‐socketed piles under service load. A micromechanics‐based rock–pile interface model with idealized nonuniform profile is proposed with two enhancements: (1) the slip line method together with nonlinear Hoek–Brown failure criteria is integrated to identify the critical shear displacement of rock asperity; and (2) the residual stage of shear behavior is properly considered with the rounding progress of sheared rubbles. The enhanced interface model is first validated by the direct shear test results under constant normal stiffness. Then, the interface model is implemented via user‐defined FRIC into the finite element code ABAQUS without the need of explicitly building the rock–pile interface profile. Accordingly, the model is applied to simulate and analyze two field cases involving rock‐socketed piles. Comparison between the predictions and field observed results shows this method can well capture the axial load transfer behavior of pile socket into weak rock. [ABSTRACT FROM AUTHOR]

Despite the extensive research on crack propagation in brittle solids, numerous unexplored problems still necessitate in-depth study. In this work, we focus on numerical modeling of multi-crack growth, aiming to explore the effect of material heterogeneity and multi-crack interaction on this process. To do this, an improved singular-finite element method (singular-FEM) is proposed with incorporation of heterogeneity and crack interaction. An efficient algorithm is proposed for simulating multi-crack propagation and interaction. Stress singularity near crack tip is reproduced by the singular elements. The singular-FEM is convenient and cost-effective, as the zone far away from crack tips is directly discretized using linear elements, in contrast to the quadratic or transition elements utilized in traditional FEM. Next, the proposed method is validated through benchmark study. Numerical results demonstrate that the superiority of the singular-FEM, which combines the merits of low cost and high accuracy. Then, the mechanics of crack growth are explored in more complex scenarios, accounting for the effects of crack interaction, loading condition and heterogeneity on crack trajectory, stress field and energy release rate. The findings reveal that the combined effect of heterogeneity and crack interaction plays a critical role in the phenomenon of crack growth, and the proposed method is capable of effectively modeling the process. [ABSTRACT FROM AUTHOR]

The effect of particle shape on the suffusion of gap-graded soils is an essential although poorly understood subject in geotechnical engineering that requires further investigation. This work presents a macroscale and microscale numerical investigation into the effect of particle shape on the suffusion of gap-graded granular materials. Rounded, elliptical, and convex particles with the same volume-equivalent diameter and varying shape coefficients were generated and used to produce samples. Next, a series of resolved coupled computational fluid dynamics (CFD) and discrete-element method (DEM) simulations were performed to provide evidence of the effect of particle shape on the suffusion susceptibility of gap-graded soils. The evolution of particle orientation, moment, and drag force coefficient were analyzed to determine the mechanisms by which particle shape exerts influence. The fine angular particles under seepage flow were found to adjust their orientation, reducing the projected area of the particle perpendicular to the fluid flow direction. Fine particles in high-flow-velocity regions had a smaller projected area and drag force coefficient. The continuous rotation of the irregularly shaped particles during suffusion implies that their migration should counteract the moments exerted by the surrounding particles. In the sample containing various irregularly shaped particles, the initial position of the most irregularly shaped particle was closer to the outlet, implying that irregularly shaped particles are less susceptible to suffusion. [ABSTRACT FROM AUTHOR]

Rigorous modeling of the stress-induced anisotropy of soils with different stress histories and loading conditions typically requires advanced constitutive models. However, calibration of state-of-the-art constitutive models can be expensive due to a large number of parameters and can encounter convergence issues when implemented in finite element codes. To circumvent these limitations, this study combines the well-known modified Cam-Clay (MCC) model with a machine learning-based multifidelity training framework, which is distinctive compared to current modeling approaches. A 'low-fidelity' neural network is first trained on synthetic data generated by the MCC model to 'learn' the model's interpretations of critical state soil mechanics. A 'high-fidelity' neural network is subsequently trained using limited experimental data to fine-tune predictions of soil behavior. The proposed framework is applied to the prediction of stress-induced anisotropy of lower Cromer till (LCT) clay. The results show that the mechanical behavior of LCT under drained and undrained triaxial compression/extension with different consolidation histories can be accurately predicted by the model. The model is also shown to be insensitive to the exact composition of the synthetic data set, specifically, the base constitutive model and parameter set used. It also shows an ability to generalize unseen data outside of the calibration space due to the underpinning soil mechanics training. Finally, explicit consideration of prediction uncertainty increases the interpretability and reliability of the proposed model toward increasing the likelihood of industry take-up. [ABSTRACT FROM AUTHOR]

Solid deformation is always a crucial factor of gas transport in sedimentary rocks. While previous studies always adopt the assumption of isotropic poroelastic deformation, anisotropic poroelastoplastic deformation is rarely considered, despite anisotropy being a ubiquitous property of natural sedimentary rocks. In this work, an anisotropic poromechanical model is established to analyze the matrix porosity and apparent permeability evolutions during the process of gas migration. Using a thermodynamic formulation that treats the fluid–solid interface as an independent phase, we derive a rate form for matrix porosity and obtain the new dissipation function that contains three parts: dissipations from solid deformation, gas adsorption, and fluid flow. For gas adsorption, we justify the rationality of the adopted model; for fluid flow, the updated porosity can be substituted into sophisticated apparent permeability models for full‐scale analysis; and for solid deformation, a recently developed constitutive model appropriate for rocks exhibiting transverse isotropy in both the elastic and plastic responses is adopted in this work. Through the novel stress‐point simulation incorporating two effective stress measures and adsorption strain, new patterns of apparent permeability are obtained, which fit the experimental data quite well and cannot be reproduced from the assumption of isotropic poroelasticity. The advantages of our poromechanical model include thermodynamic consistency and the ability to employ finite‐element‐based formulation. Finally, an initial‐boundary value problem of gas production considering anisotropic plasticity is conducted, and the effects of the bedding plane and different adsorption models are highlighted. [ABSTRACT FROM AUTHOR]

This study proposes a generalised framework for developing a hybrid machine learning (ML) model that combines support vector regression (SVR) with hyperparameter optimisation to predict thermal conductivity (k) with uncertainty. The framework contains four phases: data pre-processing, determining the best-performing hybrid model, selecting the optimal input combination, and uncertainty implementation. A database containing 2197 data points is first compiled to train the ML model. Three hyperparameter optimisation algorithms are adopted to tune hyperparameters, and their performance is evaluated by model evaluation metrics. Results show that SVR with Bayesian optimisation (SVR-BO) is the best-performing model since it produces more accurate predictions for k than models that employ grid and random searches. Given the sample insufficiency issue encountered in practice, the SVR-BO models with 144 input combinations are analysed. The compassion among models under various input combinations indicates that incorporating temperature as an additional input can provide moderate improvement in the accuracy and generalisability of the hybrid model. Based on the comparison, a five-input model is selected as the best candidate to implement the uncertainty evaluation for k. Results demonstrate that the predicted k possesses higher reliability for denser datasets and shows promising potential for applications in k with uncertainty assessments. [ABSTRACT FROM AUTHOR]

Corner breakage was less analyzed in pile–soil interaction research, causing the mechanism of particular pile load behaviors in coral sand to remain unclear. This study investigates the particle corner breakage effect on pile load performance in coral sand at both the macro-scale and particle-scale via indoor pile load model testing and Discrete Element Method and Finite Difference Method (DEM–FDM) coupled simulations. In numerical simulations, corner grains were constructed by the cluster method. Results revealed that compared with the fracture breakage, the corner breakage has a more obvious soil loosening effect. The coupled effect of corner interlock and corner breakage well explained the side friction distributions and the load share of pile tips in breakable corner grains. Besides, after obvious corner breakage, a much narrower pile-affected width around pile sides and a deeper stress transmission under pile tips were discovered in corner grains. Moreover, the decrease in effective contacts and change in soil skeletons have been proven to be essential factors behind the narrower pile-affected width and deeper stress transmission in breakable corner grains. Finally, compared with breakage effects in the previous research, the particle corner breakage effect was proved to have advantages in explaining large and sudden settlements of piles in coral sand with the use of a positive feedback loop. This study highlights the advantages of using particle corner breakage effects to explain specific pile load behaviors in coral sand and provides insight into the mechanism of pile–soil interaction in breakable granular soils. [ABSTRACT FROM AUTHOR]

The fiber‐reinforced polymer (FRP) has attracted much attention in civil engineering due to its durability and cost‐effectiveness. The soil–FRP structure interface plays an essential role when the FRP is adopted in geotechnical engineering, but its fundamental interfacial behavior remains unclear. In the present study, the atomistic models of silica representing sand, water film representing the lubricated condition, and cross‐linked epoxy representing FRP are constructed to investigate the FRP–sand interfacial properties at the nano‐scale through molecular dynamics (MD) simulations. The epoxy model geometry and forcefield are first validated by comparing the thermodynamic and mechanical parameters with experimental and simulation measurements. The silica–epoxy interfacial tribological and rheological behavior is then explored by conducting friction and creep simulations under dry and lubricated conditions. The friction force has been found linearly dependent on the normal load and increases with the sliding velocity while decreasing against water content. The modified Amontons law for the adhering surface could describe the silica–epoxy interfacial friction well. The shear stress level influences the creep characteristics with primary, secondary, and tertiary (or rupture) creep modes. The results of this study at the nano‐scale can be further developed to enhance the current contact laws of sand–FRP structure in micromechanics‐based modeling approaches. [ABSTRACT FROM AUTHOR]

The fiber‐reinforced polymer (FRP) has attracted much attention in civil engineering due to its durability and cost‐effectiveness. The soil–FRP structure interface plays an essential role when the FRP is adopted in geotechnical engineering, but its fundamental interfacial behavior remains unclear. In the present study, the atomistic models of silica representing sand, water film representing the lubricated condition, and cross‐linked epoxy representing FRP are constructed to investigate the FRP–sand interfacial properties at the nano‐scale through molecular dynamics (MD) simulations. The epoxy model geometry and forcefield are first validated by comparing the thermodynamic and mechanical parameters with experimental and simulation measurements. The silica–epoxy interfacial tribological and rheological behavior is then explored by conducting friction and creep simulations under dry and lubricated conditions. The friction force has been found linearly dependent on the normal load and increases with the sliding velocity while decreasing against water content. The modified Amontons law for the adhering surface could describe the silica–epoxy interfacial friction well. The shear stress level influences the creep characteristics with primary, secondary, and tertiary (or rupture) creep modes. The results of this study at the nano‐scale can be further developed to enhance the current contact laws of sand–FRP structure in micromechanics‐based modeling approaches. [ABSTRACT FROM AUTHOR]

In order to clarify the micro‐mechanics of clay during compression, the behavior of clay subjected to one‐dimensional compression is investigated at the particle scale using discrete element method (DEM). The flaky clay particles in the simulation are approximated by clumps made of spheres. A new contact model is implemented to account for the double‐layer repulsive force, the van der Waals attractive force and the mechanical contact force. The effect of sphere arrangement in the particle clump is discussed. The DEM model is validated against experimental observations in terms of macroscopic compressibility, particle dip angle as well as the over consolidated behavior. The e‐logσv curve shows a concave‐to‐linear shape. The evolution of dip angle of clay particles indicates that particles tend to have an anisotropy with a preferential orientation towards horizontal direction. The increase of preconsolidation pressure decreases the initial compressibility due to the increase the number of mechanical contacts. The evolution of average coordination and sphere‐sphere contact shows that the majority of mechanical contacts are generated before the compressive stress reaches 100 kPa. Evolution of soil fabric is presented and discussed. [ABSTRACT FROM AUTHOR]

Spatial variability of engineering properties caused by the natural characteristic heterogeneity is a common feature in soil layers. Meanwhile, changes in soil properties during consolidation and large strains are commonly encountered during long-term consolidation analysis of soft soils; however, few studies have considered these factors. A 1-D consolidation model fully coupled with elastic viscoplastic constitutive model, called EVPC, is developed using piecewise-linear method for long-term settlement of soft clay. The EVPC incorporates the idea of 'equivalent time' to account for large strain, soil self-weight, compressibility and permeability with spatial variability and high nonlinearity, as well as creep during the consolidation process. A comparison with finite element simulations and oedometer tests with different thicknesses of soil layer under multi-stage constant loads verified the effectiveness and accuracy of the EVPC's deterministic analysis. After that, the long-term settlement and excess pore pressure distribution of Berthierville clay layer from the field test are estimated using EVPC probabilistic analysis. All measurements from the field test are within the range of corresponded closely to the high probability density results of the probabilistic analysis, and the excess pore pressure is revealed to be more sensitive to the spatial variability of soil parameters than settlement. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a unified elastoplastic model for describing both monotonic and cyclic behaviors of natural structured clay–structure and crushable sand–structure interfaces based on the bounding surface plasticity and the critical state concept with three features: (1) a more general yield surface with two shape parameters is adopted to improve the prediction of the shear yield and the critical state of the soil–structure interface, (2) the degradation of bounding surface size and the adhesive normal stress is introduced into the model to consider the shear strength reduction of natural structured clay–structure interface, and (3) the grain breakage effect on the behavior of crushable sand–structure interface is considered by incorporating the location of the critical-state line with the broadening of grain-size distribution induced by grain breakage. Then, explicit simulation schemes with substepping and error control are established for simulating interface shear tests under constant volume, constant normal load, and constant normal stiffness conditions. Finally, comparisons between experimental and numerical results indicate that the unified soil–structure interface model is able to not only predict the state-dependent monotonic stress–strain behavior of structured clay–structure or crushable sand–structure interfaces, but also reproduce the cyclic accumulative contraction and stabilization, as well as the stress degradation observed in both structured clay–structure and crushable sand–structure interfaces. [ABSTRACT FROM AUTHOR]

The interfacial properties of silica determine the intrinsic behavior of sand and are of great importance due to its widespread application in geotechnical engineering. To investigate the sand behavior from a bottom-up perspective, the molecular dynamics approach is adopted for both dry and lubricated conditions. Friction simulations are performed using a virtual spring to pull the silica slider on the substrate at a wide range of velocities when different normal loads are applied. It has been found that the orientation of the silica surface influences dry friction. The friction force for the surface with normal vector along (001) direction is larger than that on (100) surface due to anisotropic energy corrugation, and the model with incommensurability has the smallest friction force. The Prandtl–Tomlinson model could explain the stick–slip phenomenon, and as the dominant friction mechanism shifts from thermal activation to phonon excitations and the delay effect of motion transmission, the velocity dependence of the friction crosses over from the logarithmic to the linear relationship at around 10 m/s. The Amontons law for adhering surface describes the silica interfacial friction behavior well. The friction force is linearly correlated with the external normal load and remains a finite value F0 when the external load equals 0. The lubricated friction results indicate that the friction coefficient decreases against the water content, while there is a non-monotonic relationship between F0 and water content. The friction coefficient and F0 increase with the velocity in both dry and lubricated conditions in the studied velocity range (0.1–100 m/s). It should be pointed out that the obtained force–displacement relationship is fundamental and can be applied to enhance current inter-particle laws of silica sand in micromechanics-based modeling approaches. [ABSTRACT FROM AUTHOR]

In order to study the impact of internal erosion at the scale of an engineering structure, a hydro-mechanical continuous modelling approach considering suffusion is needed. It requires a relevant mechanical model for granular soils considering the f c -dependency ( f c : fines content) and a hydraulic model for suffusion to control the changes in the fines content. To this purpose, the mechanical models for granular soil, the unified modelling approaches for f c -dependency of granular soils and the hydraulic modelling of suffusion are first reviewed. Then, a hydro-mechanical model considering both suffusion and mechanical loading is developed by combining the three components. For each component of the model, alternative choices are provided. Simulations of laboratory tests as well as an example of a dike-on-foundation problem demonstrated the reliability and applicability of this coupled numerical approach for internal erosion problems. [ABSTRACT FROM AUTHOR]

The slurry penetration in tunnels driven by the slurry TBM in the highly permeable water‐bearing ground is a complicated multiphysics process, which significantly affects the stability of the excavation face but has not been fully modelled. This study establishes a novel hydro‐mechanical coupled multiphysics modelling approach for face stability analysis. Different from existing methods, the proposed one is able to quantify simultaneously the spatial and temporal variation of slurry concentration, viscosity, stagnation gradient, soil porosity and pore pressure, as well as the soil skeleton deformation induced by the transferred slurry pressure in two or three‐dimension. The spatial and temporal pressure transfer from slurry suspension to soil skeleton during slurry penetration can be obtained. The coupled formulations are implemented into a finite element package for initial and boundary value problems at the engineering scale and enhanced by the Petrov‐Galerkin weighted residual stabilisation technique. After being validated through the experimental slurry infiltration tests, it is applied to a field case analysis to predict the excess pore pressure in front of the tunnel face, which shows good agreement with the field measurement. The efficiency of the applied slurry pressure and the pressure transfer mechanism are thus verified. Comparative calculations about the influence of the pressure transfer model are presented to further improve the understanding of the pressure transfer mechanism. The proposed approach is also verified applicable to estimate the optimum slurry pressure. [ABSTRACT FROM AUTHOR]

Although particle corner breakage has been proved to be the primary mode of particle breakage for coral sand, current studies of pile penetration have continued to present the particle fracture breakage mode, causing the mechanism of pile behaviours in coral sand to remain unclear. This study investigates the particle corner-breakage effect on pile penetration in coral sand at both the macro- and microscales via indoor pile penetration model testing and three-dimensional discrete element method (DEM) simulations. According to the study findings, DEM simulations revealed that particle corner breakage has a more obvious soil contraction effect than fracture breakage. Thus, this study is the first to explain the particular turn of the pile skin friction in coral sand by the dual effects of breakable corners. Next, relationships between the particle breakage mode and the controversial breakage zone around pile tips have been accomplished. Moreover, the decrease in effective contacts and the change in soil skeleton have been proved to be essential factors behind the narrower penetration-affected width in breakable corner grains. The study suggests that neglecting the particle corner-breakage effect can lead to a hidden danger affecting engineering safety in angular granular soil. [ABSTRACT FROM AUTHOR]

During the tunnel boring machine-based tunnelling, the grout loss caused by the grout penetration and filtration in sandy soils is a complicated multiphysics process. In this study, a novel coupled hydro-mechanical modelling approach based on mixture theory is developed and implemented into a finite element code, which can qualitatively simulate the transport of grout in the soils around the tail void of the TBM tunnel, as well as predict the ground deformation induced by tunnelling and backfill grouting. Using the proposed approach, the time-dependent grout penetration area, grout loss, variation of soil porosity due to grout filtration and the ground deformation are identified. The effects of five key variables including grout pressure, duration, rheological property, filtration in soil mass and hydraulic conductivity of the soil are simulated. The results reveal that to reduce the ground disturbance and grout loss of TBM tunnelling in highly permeable soil, the grout pressure should be increased and the grout time reduced. In addition, grout materials with large permeability, dehydration and hardening rate are recommended. [ABSTRACT FROM AUTHOR]

The effect of breakable particle corners is often overlooked in research on pile–soil interaction, which hinders the understanding of the uplift pile behaviors in calcareous sand. This research examines the breakable corner effects on uplift pile–soil interaction in calcareous sand from macro to micro, through model tests and coupled discrete element method–finite difference method. Results revealed that compared to that in silica sand, the higher bearing capacity and relatively abrupt failure behavior of uplift piles in calcareous sands were attributed to the corner interlocking effect and corner breakage effect, respectively. The unstable load transmission along piles in calcareous sand was thoroughly explained by a coupled effect of corner interlocking and breakage. Furthermore, the reduction in effective contacts and alterations in soil skeletons were identified as critical factors contributing to the distinctive soil behaviors in calcareous sand. Moreover, the relative sliding distance of particles was found to be the key factor in determining the amount of corner breakages due to stress concentration at corners. Lastly, a positive feedback loop involving corner breakage effects was proposed, successfully explaining the distinctive phenomenon of uplift piles in calcareous sand. This study provides new perspectives to clarify distinctive pile–soil interaction behaviors in calcareous sand. [ABSTRACT FROM AUTHOR]

The micro- to macro-scale pipeline-soil interaction mechanism in unsaturated granular soil remains unclear. This study investigates the unsaturated suction effect on the pipeline-soil interaction undergoing lateral ground movement using coupled discrete element method and finite element method (DEM-FEM) simulations. The Johnson-Kendall-Roberts (JKR) adhesive model was used to simulate the interparticle suction while the pipe segment was simulated with finite element meshes. The findings reveal that discontinuity and large deformation occurrences in unsaturated sandy soil in model tests can be successfully modelled by the DEM-FEM method. Besides, the interparticle suction effects on contact forces, particle collision behaviours, and special soil pressure distributions near the pipeline-soil interfaces were discovered and successfully explained. Additionally, progressive soil deformation and failure behaviours in dry and unsaturated soils were compared while the partial similarity between the suction effect and buried depth effect on pipeline-soil interaction was discussed. Finally, several conclusive pipeline-soil interaction failure patterns in different interparticle-suction conditions were identified after analysing a series of particle-scale behaviours including particle trajectories, particle contacts, and particle rotation distributions. The study indicated that neglecting the unsaturated effect can result in severely underestimating the intensity of pipeline-soil interaction and misjudging the soil failure patterns. [ABSTRACT FROM AUTHOR]

Hydromechanical behaviour of unsaturated expansive soils is complex, and current constitutive models failed to accurately reproduce it. Different from conventional modelling, this study proposes a novel physics-informed neural networks (PINN)-based model utilising long short-term memory as the baseline algorithm and incorporating a physical constraint (water retention) to modify the loss function. Firstly, a series of laboratory tests on Zaoyang expansive clay, including wetting and drying cycle tests and triaxial tests, was compiled into a dataset and subsequently fed into the PINN-based model. Subsequently, a specific equation representing the soil water retention curve (SWRC) for expansive clay was derived by accounting for the influence of the void ratio, which was integrated into the PINN-based model as a physical law. The ultimate predictions from the PINN-based model are compared with experimental data of unsaturated expansive clay with excellent agreement. This study demonstrates the capability of the proposed PINN in modelling the hydromechanical response of unsaturated soils and provides an innovative approach to establish constitutive models in the unsaturated soil mechanics field. [ABSTRACT FROM AUTHOR]

• Cross contacts and interfacial flows are integrated into a unified formulation. • Over-constrained behaviour in multi-body contacts is eliminated in a simple way. • The interfacial storage capacity is well-considered in the current framework. • Our method excels in precision, convergence rate, and modelling flexibility. In the hydro-mechanical coupling analysis of multi-body contact systems, challenges arise from the fluid flow at the interface and the over-constraint at the cross line, necessitating special treatments. In this paper, a novel algorithm is developed to address these challenges comprehensively. The proposed method introduces an intermediate interface, serving as a bridge for contact stress transfer and fluid exchange between different subdomains. The intermediate interface concurrently carries the degrees of freedom related to the displacement, pore pressure, and Lagrange multiplier. As a result, the contact constraint and interfacial fluid flow can be integrated into a unified formulation, resulting in a simplified algorithm implementation; the elimination of over-constraint behaviour can also be achieved in a simple manner. Additionally, the storage capacity of interfaces is adequately considered in the current framework, enabling the accurate computation of water suction generated by the opening of contact interfaces. Finally, several numerical experiments are conducted to validate the performance of our proposed method. Results show that our method can provide a reliable computational tool for the two-phase contact problems with interfacial fluid flow. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a novel mesoscale modelling method to study the effect of steel fibres on the fracture properties of steel fibre reinforced concrete (SFRC) based on an integrated finite-discrete element method (FDEM) framework. The present method models the steel fibre of SFRC as multiple bar elements, while simulating the cement matrix using triangle elements. A comprehensive bond-slip model is also introduced between the triangle element and bar element, enabling accurate reproduction of the interaction behaviour between the concrete matrix and steel fibre. Additionally, the progressive failure process of cohesive elements in FDEM is considered as crack propagation, confirming the superiority of FDEM from continuum to discontinuum. The results of the Brazilian split test and uniaxial compression test validate the capability of the FDEM framework for mode Ι and mode II fractures, while single fibre pull-out tests and 3-point-bending beam tests provide validation of the effectiveness of the integrated FDEM framework for the mesoscale modelling of SFRC. The discussion also covers employing the mesoscale modelling method to investigate the effect of steel fibre on the fracture properties of SFRC. Owing to its merits in quantitative analysis, the present method can serve as a feasible potential tool for mesoscale modelling analysis and SFRC design. • A mesoscale modelling method for SFRC is proposed based on an integrated FDEM framework. • A slip model is integrated to capture the bond-slip behaviour between fibre and concrete matrix. • The mesoscale modelling method is successfully validated by three types of simulation cases. • Effects of steel fibre on the fracture properties of SFRC are investigated from mesoscale point of view. [ABSTRACT FROM AUTHOR]

The mechanical behavior at soil–structure interface (SSI) has a crucial influence on the safety and stability of geotechnical structures. However, the behavior of SSI under constant normal stiffness condition from micro- to macro-scale receives little attention. In this study, the frictional characteristics of SSI and the associated displacement localization under constant normal stiffness condition are investigated at both macro- and microscales by simulating a series of interface shear tests with discrete element method. The algorithm to achieve a constant normal stiffness is first developed. The macroscopic mechanical response of the interface shear tests with both loose and dense specimens at various normal stiffness is discussed in terms of shear stress, normal stress, vertical displacement, horizontal displacement and stress ratio. Then, the microscopic behaviors and properties, including shear zone formation, localized void ratio, coordination number, force chains and soil fabric, are investigated. The effect of normal stiffness is thus clarified at both macro- and microscales. [ABSTRACT FROM AUTHOR]

There is considerable potential for data‐driven modelling to describe path‐dependent soil response. However, the complexity of soil behaviour imposes significant challenges on the training efficiency and the ability to generalise. This study proposes a novel physics‐constrained hierarchical (PCH) training strategy to deal with existing challenges in using data‐driven models to capture soil behaviour. Different from previous strategies, the proposed hierarchical training involves 'low‐level' and 'high‐level' neural networks, and linear regression, in which the loss function of the neural network is constructed using physical laws to constrain the solution domain. Feedforward and long short‐term memory (LSTM) neural networks are adopted as baseline algorithms to further enhance the present method. The data‐driven model is then trained on random strain loading paths and subsequently integrated within a custom finite element (FE) analysis to solve boundary value problems by way of validation. The results indicate that the proposed PCH‐LSTM approach improves prediction accuracy, requires much less training data and has a lower performance sensitivity to the exact network architecture compared to traditional LSTM. When coupled with FE analysis, the PCH‐LSTM model is also shown to be a reliable means of modelling soil behaviour under loading‐unloading‐reloading and proportional strain loading paths. In addition, strain localisation and instability failure mechanisms can be accurately identified. PCH‐LSTM modelling overcomes the need for ad‐hoc network architectures thereby facilitating fast and robust model development to capture complex soil behaviours in engineering practice with less experimental and computational cost. [ABSTRACT FROM AUTHOR]

This study develops a novel method for reconstructing three dimensional (3D) granular grains from computed tomography (CT) images. Unlike previous studies requiring trial-and-error hyperparameters, the hybrid algorithm introduced here, integrating the random forest (RF) algorithm and enhanced by particle swarm optimization for automatic determination of hyperparameters, is the first to train the model for constituent classification and grain segmentation. In addition, and different from previous manual methods, a convolution kernel is applied to assign an initial level set function inside an individual grain and determine whether to activate the level function for automatically reconstructing 3D grains from a CT image. All results indicate the hybrid algorithm can rapidly search the optimum hyperparameters, providing a more effective way to identify the optimum RF-based model. This model segments grains with an accuracy of 90%, in comparison with a 52% accuracy achieved by the conventional watershed algorithm. The convolution kernel can accurately and automatically identify individual grains, avoiding manual assignment of an initial calculation area and ensuring grains are correctly reconstructed. Overall, the proposed method provides a more intelligent and effective way to reconstruct 3D grains from CT images. [ABSTRACT FROM AUTHOR]

Coupled hydromechanical finite-element modeling of granular soils, taking into account internal erosion, is computationally prohibitive. Alternative data-driven approaches require large data sets for training and often provide poor generalization ability. To overcome these issues, this study proposed a physics-informed multifidelity residual neural network (PI-MR-NN) modeling strategy. The model was first trained using low-fidelity data to focus on capturing the main underpinning physical laws. Subsequent training on sparser high-fidelity data was then used to calibrate and refine the model. Physical constraints, e.g., boundary conditions, were incorporated through modifications to the loss functions. Feedforward and long short-term memory neural networks were considered as the baseline algorithms for training models. The PI-MR-NN was first used to reproduce synthetic results generated by the soil constitutive model SIMSAND and a published internal erosion model. The developed data-driven model was then applied to simulate the breach of a practical dike-on-foundation case and to predict its temporal responses. All results indicated that the hydromechanical response of porous media can be accurately captured using the proposed PI-MR-NN model. The novel training strategy mitigates the dependency of model performance on the training data set and architecture of the neural network, and the use of physical constraints improves training efficiency and enhances the model's predictive robustness. [ABSTRACT FROM AUTHOR]

This study systematically presents the application of machine learning (ML) algorithms for constructing a constitutive model for soils. A genetic algorithm is integrated with ML algorithms to determine the global optimum model, and the k-fold cross-validation method is used to enhance the models' robustness. Three typical ML algorithms with formulations explicitly expressed [i.e., back-propagation neural network (BPNN), extreme learning machine (ELM) and evolutionary polynomial regression (EPR)], and two modelling strategies (i.e. total or incremental stress–strain strategies) are used. A synthetic database is first generated based on a simple constitutive model to objectively evaluate the performance of three ML algorithms and two modelling strategies. Next, the optimum ML algorithm and the well evaluated modelling strategy are applied to experimental tests for examining its robustness. All results indicate that a BPNN-based constitutive model using the incremental stress–strain strategy performs best in modelling the mechanical behaviour of soils in terms of interpolation and extrapolation abilities, followed by ELM and then EPR. [ABSTRACT FROM AUTHOR]

This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction models for soil properties. The compression index Cc and undrained shear strength su of clays are selected as examples. Variational inference (VI) and Monte Carlo dropout (MCD), two theoretical frameworks for solving and approximating BNN, respectively, are employed and compared. The results indicate that the BNN focused on identifying patterns in datasets, and the predicted Cc and su show excellent agreement with the actual values. The reliability of the predicted results using BNN is high in the area of dense datasets. In contrast, the BNN demonstrates low reliability in the predicted result in the area of sparse datasets. Additionally, a novel parametric analysis method in combination with the cumulative distribution function is proposed. The analysis results indicate that the BNN-based models are capable of capturing the relationships of input parameters to the Cc and su. BNN, with its strong prediction capability and reliable evaluation, therefore, shows great potential to be applied in geotechnical design. [ABSTRACT FROM AUTHOR]

This paper presents a novel computational-geometry-based approach to generating random packing of complex-shaped 3D particles with quantitatively controlled sizes and shapes for discrete modeling of granular materials. The proposed method consists of the following five essential steps: (1) partitioning of the packing domain into a prescribed number of random polyhedrons with desired sizes and form-scale shapes using the constrained Voronoi tessellation; (2) extraction of key points from the edges and facets of each polyhedron; (3) construction of a freeform curve network in each polyhedron based on Bézier curve fitting; (4) generation of solid particles with smooth, convex surfaces using the biharmonic-based surface interpolation of the constructed network; and (5) creation of concavity by superimposing spherical harmonic-based random noise. To ensure that the obtained shape descriptors (e.g., the elongation, flatness, roundness and convexity ratio) match the hypothesized values, an inverse Monte Carlo algorithm is employed to iteratively fine-tune the control parameters during particle generation. The ability of the proposed approach to generate granular particles with the desired geometric properties and packing is demonstrated through several examples. This study paves a viable pathway for realistic modeling of granular media pertaining to various engineering and industrial processes. [ABSTRACT FROM AUTHOR]

The commonly used trial-and-error approach on selecting appropriate inter-particle parameters in DEM simulations incurs criticism such as user dependence and high computational cost. This study proposes a new framework based on a multi-fidelity residual neural network (MR-NN) as an alternative for calibrating inter-particle parameters in rock-like bonded granular materials. The model is first trained using low-fidelity data (LF) to focus on capturing the main underpinning correlations between macroscopic elastic and strength parameters with inter-particle parameters of contact models, where the LF data is generated from micro-macro quantitative relations. Subsequent training on sparser high-fidelity (HF) data is then used to calibrate and refine the model, in which the HF data is generated from DEM simulations for rock. Feedforward neural network (FNN) is considered as the baseline algorithm for training models. The trained MR-NN with the same LF data is finally used to predict the inter-particle parameters of calcarenite and granite in DEM simulations to examine its feasibility and generalization ability. Furthermore, the robustness of the framework is verified by discussing the effect of LF data on the performance of MR-NN. All results demonstrate that the proposed method can provide a fast and accurate determination of inter-particle parameters for DEM simulation. [ABSTRACT FROM AUTHOR]

Although there has been extensive research on the coupling process, some unexplored issues still deserve deeper exploration. In this study, our focus is on analysing the characteristics of poroelastic deformation and dissipation in thermal-hydraulic-mechanical (THM) coupling in fluid-saturated porous media. First, a coupled THM model is established as an extension of our previous study, where a solution strategy is presented based on the finite Fourier transform. The formulation is a unified framework that is compatible with the existing methods, and can be naturally decoupled into the simpler HM or TM version. Then, it is verified by benchmark study. The coupling behaviours of four different geomaterials (clay, salt rock, granite and marble) are analysed and compared in detail. We focus on several aspects: (1) dominance of hydraulic and thermal effects in different geomaterials; (2) evolution of pore pressure and heat dissipation; (3) effect of geomaterial properties on poroelastic deformation; (4) characteristics of drainage effect, cooling effect and expansion relaxation. It demonstrates that the proposed model is competent to capturing the essentials in coupling process. The insights gained from the simulations are intuitive and consistent with the existing knowledge. Further study and dimensional analysis reveal that the mechanisms in the coupling process are attributed to the combined effects of the temporal evolution in poroelastic deformation and dissipation. • A unified THM model is proposed and can be naturally decoupled into HM/TM version. • A general solution strategy is presented based on the finite Fourier transform. • THM characteristics of clay, salt rock, granite and marble are thoroughly analysed. • Mechanism of poroelastic deformation and dissipation in THM coupling is explored. [ABSTRACT FROM AUTHOR]

Physics-informed deep learning (PIDL) offers innovative and powerful ways for spatio-temporal soil consolidation analysis. However, status quo applications employ physics-informed deep learning with the continuous-time scheme that necessitates sampling massive collocation points across the entire spatio-temporal domain, which can be computationally expensive in time-evolution simulation and high-dimensional models. In this paper, a physics-informed deep learning (PIDL) framework with a local time-updating discrete scheme for forward and inverse consolidation analysis is proposed and formulated by combining the local time-updating strategy and the Runge–Kutta time discretization methods with the PIDL. Furthermore, the total and local time-updating discrete time schemes fitted to PIDL to solve the time-dependent partial differential equations are investigated. The proposed model was validated across various soil consolidation cases spanning 1D to 3D single and double drainage, exponentially time-growing drainage boundary conditions, and inverse analysis to retrieve the material and model parameters. A comprehensive evaluation of PIDL with the continuous and discrete time schemes in time-dependent multi-dimensional consolidation analysis, which is of significant reference value, is performed. Numerical results manifested that PIDL with the discrete time scheme outperforms the continuous time scheme in consolidation analysis, particularly in high dimensions. The PIDL using the local time-updating strategy yielded more accurate results than the total time-updating strategy and can facilitate training with time evolution. A transfer learning model in time domain fitted to PIDL with the discrete time scheme was developed to enhance model generality and reduce the computation cost. In addition, PIDL with the discrete time scheme can unify forward simulation and inverse analysis in a unified framework requiring less training data, which can be a promising candidate to serve as a versatile surrogate modeling tool in geotechnical engineering. • A novel PIDL strategy with local time-updating discrete scheme is formulated. • Comparative studies on PIDL with continuous and discrete time schemes are performed. • Accuracy of PIDL with local time-updating scheme using transfer learning is verified. • PIDL can solve inverse problems in consolidation analysis with less training data. • PIDL is tested on multi-dimensional consolidation with diverse drainage boundaries. [ABSTRACT FROM AUTHOR]

This paper proposes a novel numerical algorithm to solve frictional contact problems in two-phase porous media, accounting for the large deformation and interface fluid flow, within the framework of the Stable Node-based Smoothed Particle Finite Element Method (SNS-PFEM). The proposed approach builds on our previous work that was only designed for single-phase large deformation contact problems. Within this work, the hydro-mechanical coupling system is discretized using equal-order interpolation, with a polynomial pressure projection method to eliminate the pore pressure instability. Surface-to-surface discretization is then applied to formulate the contact constraint of the two-phase porous medium. Using the dual Lagrange multiplier approach, a coordinate transformation is introduced to enforce the contact constraint of the solid phase. This transformation is subsequently utilized to develop a new interface fluid element, which serves as a tool to model the fluid exchange behaviour between porous media and contact gaps. Different from existing interface fluid elements, the proposed element is constructed based on the contact algorithm, thereby enabling it to handle non-conforming interfaces and tackle challenges associated with large sliding problems. Furthermore, the proposed method can maintain a well-conditioned system matrix throughout the computation process. These distinctive attributes significantly enhance the flexibility of numerical modelling. Finally, the accuracy and performance of our method are verified through four numerical examples. The results show that the proposed method can effectively simulate various geotechnical problems, including multi-layer soil consolidation, fluid-soil-structure interaction, and geological interface-controlled slope failure. [ABSTRACT FROM AUTHOR]

Understanding the effect of particle size on the shear strength of granular materials is important for geotechnical design and construction. However, previous studies show contradicting results on the relationship between particle size and shear strength. Additionally, the effect of particle breakage on this relationship has not been fully revealed. In this study, a series of biaxial tests have been simulated with the discrete element method to explore the particle-size effect of sand considering the role of particle breakage. The sand specimens have parallel particle-size distributions. The sequential breakage model has been used to simulate particle breakage, which is a combination of replacement and cluster methods. The main conclusions of this study are: (1) the relationship of peak shear strength and particle size depends on the crushability of particles and relative density of specimens; (2) the particle size and crushability have a very slight effect on the residual shear strength; and (3) at the microscale, the relationship between shear strength and particle size is positively related to the friction utilization ratio. [ABSTRACT FROM AUTHOR]

To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three‐dimensional convolutional neural network (3D‐CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotation‐equivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D‐CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost. [ABSTRACT FROM AUTHOR]

Different from conventional methodology, this study presents an intelligent approach to fast identify the grain-size distribution (GSD) of granular soils using a convolutional neural network (CNN) under a deep learning framework. A database including 279 images of granular soils with their GSDs is first created. Then, the framework of the CNN is tailored to identify GSD. The CNN-based model is trained to predict 11 grain sizes corresponding to 1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of granular soils passing (i.e., d1, d10, ..., d100) using 80% of images, followed by the model testing using the rest 20% of images. By feeding an image of a soil sample into the proposed CNN-based model, the GSD can be predicted within several seconds. The predicted GSD exhibits excellent agreement with the measured one with an average error of 2.29% on the testing sets. It can be concluded that the proposed CNN-based model successfully provides a new intelligent way to fast, accurately, and conveniently identify the GSD of granular soils through images of soils. [ABSTRACT FROM AUTHOR]

A series of uniaxial and triaxial compression tests were performed on slate samples with different diameters at different foliation orientations with respect to the direction of the major principal stress. The size effect and anisotropy in slate, as a transversely isotropic rock, were investigated, and the research focused on aspects of elastic properties, uniaxial compressive strength (UCS), triaxial compressive strength (TCS), and triaxial residual strength (TRS). In the five elastic constants for slate, only the Young's modulus parallel to the isotropic plane is size dependent. The UCS follows a descending size-effect model developed from coal. The size-effect behaviors of the UCS and TCS are similar. Two size-dependent failure criteria are proposed by incorporating the size-effect model for UCS into the modified Hoek–Brown and Saeidi failure criteria and are verified against experimental data. This is the first time that the relationship among the compressive strength, specimen size, foliation orientation and confining pressure has been comprehensively captured for transversely isotropic rock. Without an evident size effect, the anisotropic TRS has also been effectively captured by a modified cohesion loss model, and two bound equations for the brittleness index are finally proposed for transversely isotropic rock. This work promises to provide an upscaling method for determining the mechanical parameters of transversely isotropic rocks in practical engineering. [ABSTRACT FROM AUTHOR]

The influence of two-phase flows containing suspension particles, which are common in nature, on internal erosion with coupling effect of clogging remains unclear. This paper presents a three-dimensional coupled computational fluid dynamics and discrete element method (CFD–DEM) analysis of internal erosion considering different concentrations of suspension C (i.e., mass of the suspended particles in unit volume of fluid) in gap-graded granular soils with different fine fraction Fc (i.e., the percentage by mass of the fine particles in the gap-graded sample). The influences of C and Fc on the erosion and clogging behavior of soils are investigated from both the macroscopic and microscopic perspectives. It is found that for gap-graded samples being underfilled with Fc = 15%, the suspension flow (i.e., influent fluid with suspending particles) decreases the cumulative eroded fine particle loss and the increasing rate of soil hydraulic conductivity due to clogging at the top of the sample. The degree of clogging is found to jointly be determined by both constriction size distribution and the suspension concentration. Clogging in a local area usually occurs with the formation of the clusters, which have a high resistance to the drag force applied by the fluid flow. [ABSTRACT FROM AUTHOR]

Collapse of granular material is usually accompanied by long run-out granular flows in natural hazards, e.g. rock/debris flow and snow avalanches. This paper presents a novel multi-scale approach for modelling granular column collapse with large deformation. This approach employs the smoothed particle hydrodynamics (SPH) method to solve large deformation boundary value problems, while using a micromechanical model to derive the nonlinear material response required by the SPH method. After examining the effect of initial cell size, the proposed approach is subsequently applied to simulate the flow of granular column in a rectangular channel at a low water content by varying the initial aspect ratio. The numerical results show good agreement with various experimental observations on both collapse process and final deposit morphology. Furthermore, the meso-scale behaviour is also captured owing to the advantages of the micromechanical model. Finally, it was demonstrated that the novel multi-scale approach is helpful in improving the understanding of granular collapse and should be an effective computational tool for the analysis of real-scale granular flow. [ABSTRACT FROM AUTHOR]

Numerous studies have been carried out to characterize uncertainties of soil properties, and lots of multivariate databases have been compiled to make the characterization of uncertainties more realistic. However, when it comes to complex situations, that is, for complex critical state constitutive models and soil–structure interface properties, the uncertainty quantification becomes a challenge. This paper aims to quantify the uncertainties of the soil–structure interface properties from laboratory tests. A framework of uncertainty quantification based on a simplified two-dimensional Monte Carlo simulation is first proposed. To validate the performance of the framework, an enhanced hypoplastic interface model considering particle breakage is then proposed and employed in the uncertainty quantification framework. The CMA-ES algorithm is then used to calibrate the uncertainties based on the framework and the hypoplastic soil–structure interface model. The results showed the proposed framework with the enhanced model can capture the uncertainties of the soil–structure interface properties. In the studied experiments, 30 out of 42 experimental curves were found to be well calibrated based on the Kolmogorov–Smirnov normality test. Furthermore, to obtain acceptable results, based on the calibration-validation process, the effect of the selection of experiments on the calibration performance is discussed. Some suggestions on how to choose experiments to calibrate the soil–structure interface properties are summarized, which should be helpful in practice. [ABSTRACT FROM AUTHOR]

Granular materials are typically used as fills in earth structures, including foundation, embankment, and slope. The majority of these granular materials are built and maintained in unsaturated conditions. It is still an open question to develop simple and suitable models to capture the behaviors of unsaturated granular materials. This study aimed to formulate a model for describing the behaviors of granular materials under unsaturated conditions. An exponential function based on a phenomenological model was used to reproduce the asymptotic relationship between stress ratio and deviatoric strain during shearing, and a quantitative relationship considering variable void ratios was adopted to describe the changes of degree of saturation during shearing. In this new nonlinear model, nonlinear stiffness, nonlinear stress dilatancy, and critical state were introduced to enhance the model. Compared with the model for saturated soils, the new model for unsaturated soils could capture the suction effect on the behavior of stiffness and critical state. Finally, the new nonlinear model was used to simulate the behavior of saturated and unsaturated Chongqing granular fill and Beijing sand under triaxial stress conditions. The comparisons of experimental and simulated results showed that this model can consider the suction effect in reproducing the stress–strain and volumetric behavior of unsaturated granular materials. [ABSTRACT FROM AUTHOR]

This paper aims to develop the nonlinear incremental modeling approach for describing both monotonic and cyclic behaviors of the soil‐structure interface. An exponential function is adopted as an example to reproduce the asymptotic relationship between the interface shear stress ratio and the shear displacement. A stress‐dilatancy relation is developed for the shear‐induced change of interface thickness. A shear stress reversal technique is incorporated for cyclic loading effect. Then, three numerical schemes for simulating constant thickness, constant normal load, and constant normal stiffness tests are established respectively. Next, three modifications are made to enhance the model by introducing a nonlinear shear modulus, the critical state concept, and the grain breakage effect. The enhanced model is evaluated with satisfactory performance in simulating interface tests under various loading conditions. Furthermore, the extensions to other nonlinear incremental interface model and clay‐structure interface modeling with rate effect relating to drainage conditions are successfully demonstrated and discussed. [ABSTRACT FROM AUTHOR]

Soft soil subjected to cyclic loading typically exhibits an increase in excess pore pressure under undrained condition which brings the soil to an overconsolidated state. Then, under a subsequent large number of cycles (e.g. more than one million) which also takes time, the creep at overconsolidated state influences the cyclic effect and thus results in a complicated long-term undrained behavior. This paper aims to clarify this long-term undrained behavior of overconsolidated clay. The reconstituted samples are prepared from natural samples retrieved in the north of France. First, the shear strength characteristics along monotonic triaxial stress paths are identified. Then load control cyclic tests on overconsolidated specimens are conducted in fully saturated and undrained conditions. Small cyclic deviatoric stresses are applied in order to investigate more particularly the behavior under a very large number of cycles, during which an unusual pore pressure evolution is observed. To explain this, undrained triaxial creep tests are performed on reconstituted specimens with different values of OCRs under some specified stress states. The evolutions of axial strain, excess pore pressure, stress ratio, stress path, plastic strain rates and stress dilatancy during undrained creep are discussed. The additional undrained creep tests also show that two processes are simultaneously acting in a competitive manner: increase in the pore pressure due to the cyclic loading and decrease in the pore pressure because of creep. [ABSTRACT FROM AUTHOR]

The flow direction is generally different from the gravity direction in geotechnical structures or slopes, the effect of which during suffusion remains unclear. This paper presents a coupled computational fluid dynamics and discrete element method approach to simulate the particle–fluid interaction relevant to this problem. The CFD-DEM approach is first benchmarked by a classic granular system problem, which is then used to investigate the characteristics of suffusion and its impact on the mechanical behavior. Five different angles between gravity and seepage directions for gap-graded soils with two fines contents are examined. Both the macroscopic and microscopic characteristics during suffusion and triaxial loading tests are analyzed. The direction angle is found to play a significant role affecting the erosion process and the mechanical consequence of soils. The results show that the greater the angle is, the harder it is for suffusion to occur and continue. [ABSTRACT FROM AUTHOR]