Acta Geotechnica最新文献

Enzyme-induced carbonate precipitation (EICP), which precipitates calcium carbonate within the soil matrix to cement the granular grains, presents a promising bio-mediated approach for scour countermeasures. This study explores the erosion performance of bio-cemented sand in a closed-conduit flume system, investigating the effectiveness of EICP in mitigating scour and reducing erodibility. Various parameters such as curing duration, cementation degrees and urease activities are examined to understand their influence on erosion behaviors. Furthermore, the study incorporates the analysis of calcium carbonate content and crystal microstructure to provide a better understanding on the EICP mechanism in scour mitigation. These results highlight the critical role of the interaction between calcium carbonate content and crystal features in determining the effectiveness of erodibility reduction. As the precipitated amount increases, the cemented soil exhibits enhanced hydraulic erosion resistance, with the erosion mode shifting from particle erosion and aggregated detachment to chunk fracture. In other words, the mode of sediment transport essentially is affected by the variations in crystal size, crystal quantities and deposited morphology. Two predictive formulas for threshold Shields parameter and erosion rate are also developed. Notably, the cemented soil could maintain its stability under an elevated flow of 4 m/s under an EICP treatment with 1 M of urea and calcium chloride, and a curing duration of 24 h. These findings are anticipated to serve as a valuable theoretical foundation for engineering applications.

The electrical conductivity of soil is closely associated with various physical properties of the soil, and accurately establishing the interrelationship between them has long been a critical challenge limiting its widespread application. Traditional approaches in geotechnical engineering have relied on specific conduction mechanisms and simplifying assumptions to construct theoretical models for electrical conductivity. This paper adopts a different approach by using machine learning methods to predict the electrical conductivity of clay materials. A reliable dataset was generated using the quartet structure generation set to create random clay microstructures and calculate their electrical conductivity. Based on this dataset, machine learning methods such as least squares support vector machine and backpropagation neural networks outperform theoretical models in terms of prediction accuracy and resistance to interference, with a coefficient of determination (R²) exceeding 0.995 when unaffected by disturbances. The computation of Shapley values for input features aids in explicating the machine learning model. The results reveal that saturation is a key feature in predicting electrical conductivity, while porosity and constrained diameter are relatively less important. Finally, an already trained model is applied to the one-dimensional electroosmosis-surcharge preloading consolidation theory. The results of the calculations demonstrate that neglecting changes in soil electrical conductivity during electroosmosis can lead to an overestimation of the absolute values of anode excess pore water pressure and soil settlement.

Determining the shape parameters of sand particles helps to understand the geotechnical properties of sand. This study aims to determine the roundness and sphericity of Jumunjin sand utilizing artificial intelligence (AI). A dataset comprising 1000 sand particle images from Jumunjin sand was used for testing. The training set included approximately 28,000 images, created through a combination of synthetic data (5000 images) and additional data augmentation techniques to address data imbalance issues. Unlike traditional methods for determining roundness and sphericity, this research proposes a model that combines a regression model with a convolutional neural network (CNN), using ResNet and DenseNet as the backbone networks. The results, evaluated based on the coefficient of determination (R²) between the predicted values using the DenseNet169 model and the true values, yielded an R² of 0.695 for roundness and 0.979 for sphericity. When classifying based on the Krumbein and Sloss chart using the trained model, the DenseNet169 model demonstrated the highest accuracy (73.6%), precision (77.9%), and recall (77.2%). A comparison between AI predictions and human evaluations revealed considerable variation in human classification, depending on the observers, whereas the AI model consistently exhibited robust performance in determining both roundness and sphericity.

In this paper, three-dimensional nonlinear dynamic finite-element analyses are conducted to examine the effect of soil property variability on the lateral displacement (D) of liquefiable ground reinforced by granular columns. A suite of 20 ground motions is selected from the NGA-West2 database as input. A soil-granular column ground system consisting of an intermediate liquefiable layer is modeled in OpenSees. Both the random variable (RV) and random filed (RF) methods are adopted to model the variability of soil property parameters. Dynamic analyses are then conducted to estimate the earthquake-induced deformation of the soil-granular column system. It is found that modeling the variability of soil parameters based on the RV method generally increases the geometric mean and standard deviation (σ_lnD) of D for the soil-granular column system. Enlarging the spatial correlation of soil parameters in the RF model brings in a slight increase of the mean D and comparable σ_lnD values, respectively. Hence, incorporating the spatially correlated soil property parameters may not be necessarily increase the variation of D for the soil-granular column system. Specifically, the statistical distribution of D is more sensitive to the vertical scale of fluctuation rather than the horizontal one. The results presented could aid in addressing the variability issue for performance-based design of granular column-reinforced liquefiable ground in engineering applications.

Liquefaction-induced flow failures, excessive settlements, lateral spreading, and loss of shear strength in granular soils can become massive hazards during earthquakes. Among the various mitigation techniques available, soil reinforcement using dense granular columns can be considered as a very effective technique, and its effectiveness gets further improved by encasing the columns in a geotextile to maintain the integrity of the columns during earthquakes. This paper presents findings from a first-of-its-kind study of simple shear tests on sand reinforced with geotextile-encased granular columns (EGC) to understand the fundamental mechanisms leading to its improved liquefaction resistance and shearing response. The effects of area replacement ratio and grouping action of columns on the overall response are established by performing a series of multi-stage constant volume simple shear tests on unreinforced and EGC-reinforced sands. The area replacement ratio was varied between 4 and 16% in different tests, and the tests with 16% area replacement ratio were conducted on sand with a single column and a group of columns. The particle sizes, encasement tensile strength, and column configurations are carefully chosen to avoid scaling and boundary effects on the test results. The performance of EGCs against liquefaction was evaluated considering all fundamental mechanisms, including the progression of pore pressures, nonlinear hysteretic behaviour, strain energy accumulation, and shear modulus degradation. The potential of EGCs for mitigating the liquefaction and improving the post-liquefaction shear strength of sand was found to improve with the increase in the area replacement ratio. For a specific area replacement ratio, the beneficial effects were more significant when the EGCs were spread into a group of symmetrically placed columns instead of a single column at the centre. The stress concentration on the EGCs due to modulus contrast and additional confinement offered by the EGCs to intervening soil have collectively benefited the shearing response of sand before, during, and after liquefaction.

Numerical models based on seepage-deformation coupling governing equations are often used to simulate soil hydrodynamics and deformation in unsaturated porous media. Among them, Picard iteration method with pressure head as the main variable is widely used because of its simplicity and ability to deal with partial saturation conditions. It is well known that the method is prone to convergence failure under some unfavorable flow conditions and is also computationally time-consuming. In this study, the soil–water characteristic curve (SWCC) of unsaturated soil described by the exponential function is used to linearize the coupling equations to overcome the repeated assembly of nonlinear ordinary differential equations. The finite element method with six-node triangular element is used to discretely linearize the coupling governing equations. Further, the classical Gauss–Seidel iterative method (GS) can be used to solve the linear equations generated from the linearized coupling equations. However, the convergence rate of GS seriously restricts the ill-condition of the linear equations, especially when the condition number of linear equations is much larger than 1.0. Thus, we propose an improved Gauss–Seidel iterative methods MP(m)-GSCMGI by combining multistep preconditioning and cascadic multigrid. The applicability of the proposed methods in simulating variably saturated flow and deformation in unsaturated porous media is verified by numerical examples. The results show that the proposed improved methods have faster convergence rate and computational efficiency than the conventional Picard and GS. The hybrid improved method MP(m)-GSCMGI can achieve more robust convergence and economical simulation.

Bio-mediated methods, such as microbially induced carbonate precipitation, are promising techniques for soil stabilisation. However, uncertainty about the spatial distribution of the minerals formed and the mechanical improvements impedes bio-mediated methods from being translated widely into practice. To bolster confidence in bio-treatment, non-destructive characterisation is desired. Seismic methods offer the possibility to monitor the effectiveness and mechanical efficiency of bio-treatment both in the laboratory and in the field. To aid the interpretation of shear wave velocity measurements, this study uses the discrete element method to examine the small-strain stiffness of bio-cemented sands. Bio-cemented specimens with different characteristics, including properties of the host sand (void ratio, uniformity of particle size distribution) and properties of the precipitated minerals (distribution pattern, content, Young’s modulus), are modelled and subjected to static probing. The mechanisms affecting the small-strain properties of cemented soils are investigated from microscopic observations. The results identify two mechanisms controlling the mechanical reinforcement associated with bio-cementation, namely the number of effective bonds and the ability of a single bond to improve stiffness. The results show that the dominant mechanism varies with the properties of the host sand. These results support the use of seismic measurements to assess the mechanical efficiency and effectiveness of bio-mediated treatment.

Liquid nitrogen is the most common refrigerant adopted in the artificial ground freezing (AGF) method for the rapid freezing of soil. However, the relatively high price of liquid nitrogen demands the reuse of liquid nitrogen in AGF, which utilizes partially gasified liquid nitrogen after an initial injection. This study investigated the reusability of liquid nitrogen in AGF by performing a field experiment. Temperatures of the ground were monitored near the sub-freezing pipes installed 1 m away from the main freezing pipes, where liquid nitrogen was initially injected. A frozen wall having a thickness of 1 m was formed between two sub-freezing pipes after 5 days of injecting liquid nitrogen into the main freezing pipes. Furthermore, the lowest temperature of  − 12 °C measured in the sub-freezing pipe implied that the temperature of nitrogen after circulating through the main freezing pipe was sufficiently low to freeze the surrounding soil formation. The freezing rate, elapsed time for freezing, and freezing duration evaluated from the monitored temperature data also demonstrated the promising potential of reusing liquid nitrogen in AGF for saturated silty deposits.

This paper examines the shaft resistance mobilisation ratio as a predictor of cumulative displacement of small-scale floating and end-bearing energy pile foundations subjected to vertical compressive loads embedded in dry sandy soils. A reduced friction model pile was subjected to different mechanical loads and two long-duration, cyclic heating/recovery temperature changes. The pile, soil and container temperatures, pile strains, and vertical displacements are monitored, analysed, and discussed. The results further validate numerical analyses that propose the shaft resistance mobilisation ratio as a variable to identify thresholds above which permanent cyclic thermo-induced deformations may occur. Overall, the experimentally observed responses indicate incremental deformations as the shaft resistance mobilisation ratio increased. The results also suggest that a mobilisation ratio of 66% could be a potential conservative lower-bound limit that could control the increment of thermal-induced vertical displacements in the long term under free pile head conditions. This suggests that a performance-based design would be a reasonable approach for energy piles, and monitoring programs should be set in the field before loading and thermo-activation.

This paper presents an elastoplastic model to estimate the hydromechanical behavior of unsaturated soils based on state boundary hypersurface. Through mechanical hypersurface, the influence of saturation on yield stress can be expressed in a full form rather than an incremental form. Two hydraulic hypersurfaces and one mechanical hypersurface are proposed to establish the model. Two hydraulic hypersurfaces, composed of degree of saturation, void ratio and matrix suction, define the plastic hydraulic boundary. The elastic hydraulic behavior of unsaturated soils can be represented by scanning lines between these two hydraulic hypersurfaces. The mechanical hypersurface, composed of degree of saturation, void ratio and effective stress, defines the plastic mechanical boundary. The elastic mechanical behavior of unsaturated soils can be represented by scanning lines below the mechanical hypersurfaces. A large number of laboratory tests are used to validated the proposed model, showing that it can reasonably capture important features of the hydromechanical behavior of unsaturated soils.