Geotechnics

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis. Full article

Currently, Japan’s fishing industry is facing a severe decline in its workforce. As a response, fishing mechanization using small underwater robots is promoted. These robots offer advantages due to their compact size, although their operating time is limited. A major source of this limited operating time is posture stabilization, which requires continuous thruster use and rapidly drains the battery. To reduce power consumption, anchoring the robot to the seabed with anchors is proposed. However, due to neutral buoyancy, the available thrust is limited, making penetration into the seabed difficult and reducing stability. To address this, we focus on composite-shaped anchors and vibration. The anchors combine a conical tip and a cylindrical shaft to achieve both penetrability and holding force. However, a trade-off exists between these functions depending on the tip angle; anchors with larger angles provide better holding capacity but lower penetrability. To overcome this limitation, vibration is applied to reduce soil resistance and facilitate anchor penetration. While vibration is known to aid penetration in saturated soft soils, the effect of tip angle under such conditions remains unclear. This study aims to clarify the optimal tip angle for achieving sufficient penetration and holding performance under vibratory conditions. Experiments in underwater saturated soft soil showed that vibration improves both penetration and holding. This effect was strong in anchors with tip angles optimized for holding force. These findings support the development of energy-efficient anchoring systems for autonomous underwater operations in soft seabed environments. Full article

Accurate evaluation of subgrade behaviour under dynamic loading is essential for the long-term performance of transport infrastructure. While the Light Weight Deflectometer (LWD) is commonly used to assess subgrade stiffness, it provides only a single stiffness value and may not fully capture the time-dependent response of soil. This study presents an image-based vision system developed to monitor soil surface displacements during loading, enabling more detailed analysis of dynamic behaviour. The system incorporates high-speed cameras and MATLAB-based computer vision algorithms to track vertical movement of the plate during impact. Laboratory and field experiments were conducted to evaluate the system’s performance, with results compared directly to those from the LWD. A strong correlation was observed (R² = 0.9901), with differences between the two methods ranging from 0.8% to 13%, confirming the accuracy of the vision-based measurements despite the limited dataset. The findings highlight the system’s potential as a practical and cost-effective tool for enhancing subgrade assessment, particularly in applications requiring improved understanding of ground response under repeated or transient loading. Full article

The insertion into the soil stratum to be evaluated is the factor that most affects the results obtained by the vane shear test (VST). According to the literature, it has been identified that there is a disturbance in the fabric and even in the movement of soil particles around the probe. The current study allowed the VST to be carried out on kaolinitic clayey soils reconstituted in the laboratory at different historical preconsolidation artificial stresses. The influence of the disturbance on the alteration of the soil analysed is directly linked to the thickness of the vane blades and their corresponding vane area ratio (VA). For this reason, a digital image correlation (DIC) technique was proposed to analyse images taken during the test’s development. The alteration produced by the disturbance was recorded, and the result obtained was compared with previous studies. This analysis established the effect on the reconstituted samples by employing a disturbance parameter specific to this study. Full article

The increasing difficulty of handling industrial and agricultural wastes has generated interest in reusing materials such as Cement Kiln Dust (CKD) and Rice Husk Ash (RHA) for sustainable soil stabilization. This study examined the enhancement of lateritic soil with the incorporation of CKD (0–12%) and RHA (0–25%) by weight. An integrated experimental and Artificial Neural Network (ANN) methodology was utilized to evaluate and forecast geotechnical features. Laboratory assessments were conducted to measure Atterberg limits, Maximum Dry Density (MDD), Optimum Moisture Content (OMC), and Unconfined Compressive Strength (UCS) at 0, 7, and 28 days of curing. The results indicated significant enhancements in soil characteristics with CKD-RHA combinations. Artificial Neural Network models, including GELU, LOGSIG-3, and Leaky ReLU activation functions, accurately predicted the UCS, MDD, and OMC, achieving R² values as high as 0.980. This work underscores the efficacy of CKD-RHA mixtures in improving soil stability and the promise of ANN models as excellent prediction instruments, fostering sustainable and economical construction methodologies. Full article

Soil moisture is one of the key hydrologic components indicating the performance of landfill final covers. Conventional compacted clay (CC) covers and evapotranspiration (ET) covers often suffer from moisture-induced stresses, such as desiccation cracking and irreversible hydraulic conductivity. Engineered turf (EnT) cover systems have been introduced recently as an alternative; however, their field-scale moisture distribution behavior remains unexplored. This study investigates and compares the soil moisture distribution characteristics of EnT, ET, and CC landfill covers at a shallow depth using one year of field-monitored data in a humid subtropical region. Three full-scale test Sections (3 m × 3 m × 1.2 m) were constructed side by side and instrumented with moisture sensors at a depth of 0.3 m. Distributional characteristics of moisture were evaluated with descriptive statistics, goodness-of-fit tests such as Shapiro–Wilk (SW) and Anderson–Darling (AD), Gaussian probability density functions, Q–Q plots, and standard-normal transformations. Results revealed that Shapiro–Wilk (W = 0.75–0.92, p < 0.001) and Anderson–Darling $(A^2=1.63\times {10}^3\mathrm{to}6.31\times {10}^3,p<0.001)$ tests rejected normality for every cover, while Levene’s test showed unequal variances between EnT and the other covers $(F>5.4\times {10}^4,$$p<0.001)$ but equivalence between CC and ET (F = 0.23, p = 0.628). EnT cover exhibited the narrowest moisture envelope $(95\%\mathrm{range}=0.156\mathrm{to}0.240{\mathrm{m}}^3/{\mathrm{m}}^3;\mathrm{CV}=10.6\%)$, whereas ET and CC covers showed markedly broader distributions (CV = 38.6 % and 33.3 %, respectively). These findings demonstrated that EnT cover maintains a more stable shallow soil moisture profile under dynamic weather conditions. Full article

This research presents the calibration of a constitutive model to replicate the cyclic performance of soils using a Bayesian framework. This study uses data from laboratory-conducted consolidated undrained isotropic cyclic triaxial tests and numerical tools to estimate optimal parameters by the application of Slice Sampling in a Bayesian analysis and to determinate the uncertainty of the model. For each calibrated parameter in the model, a probability distribution was obtained from the Markov chain. The means and the standard deviations from the distributions are compared with the laboratory results by the simulation of a series of consolidated undrained isotropic cyclic triaxial tests and a numerical model for a deposit that replicates the Wildlife’s stratigraphic characteristics. The calibrated model response offers a good approximation of the recorded data and the uncertainty due to the model is evaluated. The results of this study demonstrate that Bayesian calibration can reliably quantify parameter uncertainty, reveal parameter correlations that deterministic methods overlook, and improve confidence in liquefaction assessments. This probabilistic framework provides a robust basis for extending calibration to other soil types and site conditions. Full article

Internal erosion is a significant issue caused by water flow within soils, resulting in structural collapse of hydraulic structures, particularly in coarse soils located near rivers. These soils typically exhibit granulometric instability due to low clay content, resulting in poor hydraulic and mechanical properties. To mitigate this problem, cement treatment is applied as an alternative to soil removal, reducing transportation and storage costs. The hole erosion test (HET) and Crumbs tests, shearing behaviour through consolidated undrained (CU) triaxial, and microstructure analyses regarding scanning electron microscopy (SEM), mercury intrusion porosimeter (MIP) and thermogravimetric analysis (TGA) were conducted for untreated and treated coarse soil specimens with varying cement contents (1%, 2%, and 3%) and curing durations (1, 7, and 28 days). The findings indicate a reduction in the loss of eroded particles and overall stability of treated soils, along with an improvement in mechanical properties. SEM observations reveal the development of hydration gel after treatment, which enhances cohesion within the soil matrix, corroborated by TGA analyses. MIP reveals the formation of a new class of pores, accompanied by a reduction in dry density. This study demonstrates that low cement addition can transform locally unsuitable soils into durable construction materials, reducing environmental impact and supporting sustainable development. Full article

Geotechnical engineering faces challenges related to data, especially the ones related to dynamic soil behavior (i.e., shear modulus reduction and damping ratio curves with strain), with only a few datasets in open-access format and a slow transition to a more data-driven method. This lack of data, combined with variations in data collection methods, makes it difficult to build accurate predictive models. These challenges arose while developing a model to predict the shear modulus curves, an important soil property to better understand seismic hazard from three different databases. Combining multiple databases can sometimes degrade model performance. To address this, a novel approach in geotechnics based on Shapley values computed from an XGBoostRegressor model is introduced. This game–theoretic method quantifies each database’s marginal contribution to the model’s R² across all possible combinations, making it possible to identify which databases contribute most to improving performance. As the number of available databases continues to grow, this method will become increasingly useful. For shear modulus reduction curves, two out of three databases explored have Shapley values of 0.341 and 0.339, while the last one reaches only a value of 0.320. This suggests that the first two databases contribute more to the model’s performance. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

This study investigates the influence of time (ageing) on the uplift capacity of bored piles in cohesionless silty sand through a full-scale field testing programme. Four reinforced concrete piles, two shorter (16 m) and two longer (21 m), were installed and tested under axial tension at two different ageing intervals: 35 days and 165 days post-construction. The load-displacement behaviour, load transfer characteristics, and shaft friction mobilisation were monitored using load cells and embedded strain gauges. Results showed that while all piles exhibited similar ultimate capacities, the aged piles consistently demonstrated stiffer responses and earlier mobilisation of shaft resistance. Extrapolated estimates showed modest increases in estimated ultimate uplift capacity, ranging from 2% to 7%, with ageing. Strain gauge data also indicated more uniform load transfer in the aged piles, suggesting time-dependent improvements in pile-soil interface behaviour. The findings confirm that even in cohesionless silty sand, moderate ageing effects can enhance uplift performance, but the extent of improvement is small and variable. These findings provide a valuable reference for evaluating uplift design assumptions and interpreting field test behaviour in similar soil environments. Full article

Synthetic rock mass (SRM) models commonly represent fractures as planar surfaces, potentially oversimplifying the complex geometries observed in natural rock masses. This study investigates whether incorporating large-scale fracture undulations significantly affects predicted rock-mass strength compared to conventional flat joint representations. Using the Finite-Discrete Element Method (FDEM), we analyzed multiple discrete fracture network (DFN) configurations under uniaxial and biaxial loading conditions, comparing models with geometrically simplified planar fractures against those incorporating conceptual undulated surfaces. Results reveal counterintuitive and inconsistent patterns across different DFN geometrical realizations, demonstrating that network topology and connectivity patterns govern overall behaviour more than individual fracture geometry. These findings challenge assumptions that geometric simplification can be systematically compensated through parameter adjustments. However, given that detailed fracture characterization data are typically unavailable until design completion, and even accessible rock outcrops provide only limited 2D surface exposures of inherently 3D fracture networks, pursuing sophisticated geometric representations may be impractical. Instead, engineering practice should focus on quantifying inherent variability bounds. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

Drag embedment anchors (DEAs) play a vital role in maintaining the stability and safety of offshore structures, including floating wind turbines, oil rigs, and marine renewable energy systems. Accurate prediction of anchor performance is essential for optimizing mooring system designs, reducing costs, and minimizing risks in challenging marine environments. By leveraging advanced machine learning techniques, this research provides innovative solutions to longstanding challenges in geotechnical engineering, paving the way for more efficient and reliable offshore operations. The findings contribute significantly to developing sustainable marine infrastructure while addressing the growing global demand for renewable energy solutions in coastal and deep-water environments. This current study evaluated tree-based machine learning algorithms, e.g., decision tree regression (DTR) and random forest regression (RFR), to predict the holding capacity and efficiency of DEAs in sand seabed. To train and validate the results of machine learning models, the K-fold cross-validation method, with K = 5, was utilized. Eleven geotechnical and geometric parameters, including sand friction angle (φ), fluke-shank angle (α), and anchor dimensions, were analyzed using 23 model configurations. Results demonstrated that RFR outperformed DTR, achieving the highest accuracy for capacity prediction (R = 0.985, RMSE = 344.577 KN) and for efficiency (R = 0.977, RMSE = 0.821 KN). Key findings revealed that soil strength dominated capacity, while fluke-shank angle critically influenced efficiency. Single-parameter models failed to capture complex soil-anchor interactions, underscoring the necessity of multivariate analysis. The ensemble approach of RFR provided superior generalization across diverse seabed conditions, maintaining errors within ±10% for capacity and ±5% for efficiency. Full article

It is an urgent issue for preventing slope failure caused by increasingly severe earthquakes and heavy rain. As a conventional construction method, reinforcing bar insertion work uses the tensile force of the core bar to integrate multiple core bars and pressure plates. Meanwhile, landslide deterrence piles are a construction method in which steel or concrete piles are constructed below the slope, and the rigidity of the piles is used to resist slope failure. In this study, these methods are combined to propose a reinforcing bar insertion work that uses pipes as a construction method. The pipes are not embedded in the immovable layer and are not connected to the reinforcing bar insertion work; therefore, the construction is expected to be simple. Two series of model experiments—a lift-up experiment and a water sprinkling experiment—were performed. Through the lift-up experiment, the effectiveness of the proposed method against static load was confirmed, and the evaluation formula of the load applied to the core bar was proposed. Through the water sprinkling experiment, the effectiveness against rainfall was confirmed, that is, the time until slope failure was extended by the proposed method. Full article

Legged rovers are gaining interest for planetary exploration due to their high mobility. However, loose regolith on celestial surfaces like the Moon and Mars often leads to slippage as legs disturb the soil. To address this, a walking technique has been proposed that enhances soil support by transmitting vibrations from the robot’s legs. This approach aims to improve mobility by increasing the ground’s bearing capacity. To evaluate its effectiveness in space-like environments, this study experimentally investigates the effect of vibration on bearing capacity under low atmospheric pressure, which can influence soil behavior due to reduced air resistance. Using Silica No. 5 and Toyoura sand as test materials, experiments were conducted to compare bearing capacities under standard and low pressure. The results demonstrate that applying vibration significantly improves bearing capacity and that the influence of atmospheric pressure is minimal. These findings support the viability of vibration-assisted locomotion for planetary rovers operating in low-pressure extraterrestrial environments. Full article

Previous studies have extensively applied the generalized consolidation theory, which incorporates a two-stress state variable framework, to predict the volumetric behavior of unsaturated expansive soils under varying mechanical stress and matric suction. A key requirement for this approach is a constitutive surface that links the soil void ratio to both net stress and matric suction. A large number of fitting parameters are typically needed to accurately fit a two-variable void ratio surface equation to laboratory test data. In this study, a single-stress state variable framework was adopted to describe the void ratio as a function of effective stress for unsaturated soils. The proposed approach was applied to fit void ratio–effective stress constitutive curves to laboratory test data for two different expansive clays. Additionally, a finite element model coupling variably saturated flow and stress–strain analysis was developed to simulate the volume change behavior of expansive clay subjected to moisture fluctuations. The model utilizes suction stress to compute the effective stress field and incorporates the dependency of soil modulus on volumetric water content based on the proposed void ratio–effective stress relationship. The developed numerical model was validated against a benchmark problem in which a layer of Regina expansive clay was subjected to a constant infiltration rate. The results demonstrate the effectiveness of the proposed model in simulating expansive soil deformations under varying moisture conditions over time. Full article

The strength mobilisation framework was adopted for the first time to describe the stress–strain responses for three different types of sands, including a total of 30 published drained triaxial tests—25 for Karlsruhe Fine Sand, 2 for Ottawa sands and 3 for Fontainebleau sand, under confining pressures ranging from 50 to 400 kPa. The peak shear strength ${\tau }_{peak}$ obtained from drained triaxial shearing of these sands was used to normalise shear stress. Shear strains normalised at peak strength ${\gamma }_{peak}$ and at half peak of shear strength ${\gamma }_{M=2}$ were taken as the normalised reference strains, and the results were compared. Power–law functions were then derived when the mobilised strength was between $0.2{\tau }_{peak}$ and $0.8{\tau }_{peak}$. Exponents of the power–law functions of these sands were found to be lower than in the published undrained shearing data of clays. Using ${\gamma }_{M=2}$ as the reference strain shows a slightly better power–law correlation than using ${\gamma }_{peak}$. Linear relationships between the reference strains and variables, such as relative density, relative dilatancy index, and dilatancy, are identified. Full article

Freezing and thawing cycles significantly affect the mechanical and hydraulic behavior of soils, posing detrimental challenges for infrastructures in cold climates. This study develops and validates a coupled Thermal–Hydraulic–Mechanical (THM) model using COMSOL Multiphysics (Version 6.3) to demonstrate the complexities of vapor and water flux, heat transport, frost heave, and vertical stress build-up in unsaturated soils. The analysis focuses on fine sand, sandy clay, and silty clay by examining their varying susceptibilities to frost action. Silty clay generated the highest amount of frost heave and steepest vertical stress gradients due to its high-water retention and strong capillary forces. Fine sand, on the other hand, produced a minimal amount of frost heave and a polarized vertical stress distribution. The study also revealed that vapor flux is more noticeable in freezing fine sand, while silty clay produces the greatest water flux between the frozen and unfrozen zones. The study also assesses the impact of soil properties including the saturated hydraulic conductivity, the particle thermal conductivity, and particle heat capacity on the frost-induced phenomena. Findings show that reducing the saturated hydraulic conductivity has a greater impact on mitigating frost heave than other variations in thermal properties. Silty clay is most affected by these changes, particularly near the soil surface, while fine sand shows less noticeable responses. Full article