Geotechnics

The shear strength and compression properties of stiff Boom clay from Belgium at a depth of about 16.5 to 28 m were investigated by means of cone penetration and laboratory testing. The latter consisted of index classification, constant rate of strain, triaxial, direct simple shear and unconfined compression tests. The Boom clay samples exhibited strong swelling tendencies. The suction pressure was measured via different procedures and was compared to the expected in situ stress. The undrained shear strength profile determined from cone penetration tests (CPTs) was not compatible with the triaxial and direct simple shear measurements, which gave significantly lower undrained shear strength values. Micro-computed tomography (μCT) scans of the samples showed the presence of pre-existing discontinuities which may cause inconsistencies in the comparison of the laboratory test results with in situ data. The experimental data gathered in this study provide useful information for analyzing the mechanical behaviour of Boom clay at shallow depths considering that most investigations in the literature have been carried out on deep Boom clay deposits. Full article

In the exploration missions for Mars and the Moon, rovers with legs as mobility mechanisms are necessitated owing to their high mobility. However, the surface of Mars and the Moon is loose, leading the rovers to slip by virtue of the ground easily deforming due to the leg movements of the rover. A walking method aimed at preventing slippage was proposed to address this issue. Prior studies have confirmed that applying vibrations increases the shear strength of the ground and sinkage of the rover legs, thereby enhancing bearing capacity, that is, the resistance force exerted on the legs of the rover by the ground. Identifying the optimal vibration is crucial for maximizing performance. This study investigated the relationship between bearing capacity and vibration acceleration, revealing a correlation between the peak bearing capacity and the main vibration acceleration spectra. This finding provides insight into determining the optimal time for imparting vibrations to the ground, thereby improving the performance of space exploration rovers. Full article

The “rail track superstructure–subgrade” system is a sophisticated engineering structure critical in ensuring safe and efficient train operations. Its analysis and design rely on mathematical modeling to capture the interactions between system components and the effects of both static and dynamic loads. This paper offers a detailed review of contemporary modeling approaches, including discrete, continuous, and hybrid models. The research’s key contribution is a thorough comparison of five primary methodologies: (i) quasi-static analytical calculations, (ii) multibody dynamics (MBD) models, (iii and iv) static and dynamic finite element method (FEM) models, and (v) wave propagation-based models. Future research directions could focus on developing hybrid models that integrate MBD and FEM to enhance moving load predictions, leveraging machine learning for parameter calibration using experimental data, investigating the nonlinear and rheological behavior of ballast and subgrade in long-term deformation, and applying wave propagation techniques to model vibration transmission and evaluate its impact on infrastructure. Full article

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation. Full article

Oil sands tailings consist of a combination of sand, fine particles, water, and residual unextracted bitumen in varying ratios. The management of these mine waste tailings is largely influenced by their consolidation behavior. Large strain consolidation testing, such as the multi-step large strain consolidation (MLSC) test, is commonly used to determine consolidation properties but requires considerable time. A benchtop centrifuge (BTC) apparatus was proposed to derive the consolidation parameters of the following three clay-rich oil sands tailings slurries: two samples of high-plasticity fluid fine tailings (FFT) and one of low-plasticity FFT. Comparison with the MLSC tests illustrates that the BTC-derived compressibility data closely matched the MLSC test’s compressibility curve within the BTC stress range. However, the hydraulic conductivity from the BTC test was an order of magnitude higher than that from the MLSC test. The consistency of the BTC method and the validation of scaling laws were confirmed through modeling-of-models tests, showing a consistent average void ratio regardless of the specimen height or gravity scale. The influence of the small radius of the BTC was found to be minimal. The limitations of the BTC in the physical modeling of the consolidation behavior are discussed and their impact on the interpretation of the observed consolidation behavior is addressed. Overall, the BTC test provides a rapid method to gain insight on high-water-content slurries’ large strain consolidation behavior. Full article

The efficiency of heat transfer through borehole heat exchangers is influenced by the thermal resistances of both the borehole and the surrounding soil. Optimizing these resistances can improve the heat transfer performance and reduce system costs. Soil thermal resistance is geographically specific and challenging to reduce, according to previous research; in contrast, borehole resistance can be minimized through practical approaches, such as increasing the thermal conductivity of the grout or adjusting the shank spacing in the U-tube configuration. The previous literature also suggests that coaxial pipes are a more efficient design than a single U-tube borehole heat exchanger. A novel approach involves inserting a physical barrier between the U-tube’s inlet and outlet legs to reduce the thermal short-circuiting and/or to improve the temperature distribution from the inlet leg in a U-tube borehole. Limited studies exist on the barrier technique and its contribution to reducing thermal resistance. The effects of two different barrier geometries, flat plate and U-shape, made from different materials, with various grout and soil thermal conductivities and shank spacing configurations, were considered in this study. Using FlexPDE software version 6.51, this study numerically assesses thermal resistances through the borehole. This study focuses on the sole contribution of a barrier in mitigating the thermal resistance of a U-tube borehole heat exchanger. This study suggests that the barrier technique is an effective solution for optimizing heat transfer through U-tube borehole heat exchangers, especially with reduced shank spacing and lower thermal conductivity soil. It can reduce the length of a U-tube borehole by up to 8.1 m/kW of heat transfer, offering a viable alternative to increasing shank spacing in the U-tube borehole or the enhancing thermal conductivity of the grout. Moreover, under specific conditions of soil and grout with low to medium thermal conductivity, a U-tube borehole heat exchanger with a barrier between the legs demonstrates a reduction of up to 43.4 m per kW heat transfer (22.7%) in the overall length compared to coaxial pipes. Full article

The 2023 storm Daniel hit areas of Greece, Bulgaria, Turkey and Libya, leading to severe flooding phenomena. One of the severely affected areas was the Thessaly Region in central Greece, which was subjected to extreme precipitation, with historic record rainfalls. This paper presents an overview of the observed damage to the built environment (buildings, bridges, slopes, etc.) and the resulting soil response or soil–structure interaction phenomena associated with the severe flooding caused by storm Daniel. To assist readers, reported cases of damage and supporting evidence (such as photos, rainfall level, etc.) are introduced in an interactive map of the affected area, illustrating the spatial effects of this severe storm on the built environment. Full article

The epikarst (the subsurface cavernous part of karstic rock studied in the Bakony Regions, the Mecsek Mountains and the area of Pádis) was compared across several karst sample sites. Since the degree of cavity formation in the epikarst cannot be studied directly, statistical analysis of the measured resistivity values was used to determine and compare the characteristics of the epikarst at different sample sites and, thus, the associated karst areas. For this, the significance of bedrock resistivity values obtained by Vertical Electrical Sounding (VES) was determined by t-tests. The mean values and standard deviations along the profiles of the VES measurement sites were calculated and graphically represented. It was established that the epikarst of profiles with high resistivity mean values is thicker, and the epikarst is of heterogeneous cavity formation (cavity formation is of different degrees) at sites where the standard deviation of resistivity is high. The epikarst of some karst sample sites can be compared by their standard deviation fields since in areas with higher resistivity, a thicker epikarst results in more expanded cavities and a lower water table, while heterogeneous cavity formation causes different cavity sizes and different resistivities. At sites where the standard deviation fields overlap with each other, their epikarsts are similar, at those where they do not overlap, they are different, and at sites where the fields touch, their similarity is transitional. If the standard deviation fields overlap each other, those with higher mean values and higher standard deviation have more cavities and their cavity formation is more heterogeneous. The epikarst with these characteristic features is regarded as more mature because at a lower water table, a higher arithmetic mean of resistivity and a higher standard deviation can be established. The reliability of the comparisons is shown by the fact that those with a more mature epikarst are karstified to a greater degree. Full article

This experimental study examines the effects of landfill leachate contamination on soil hydraulic conductivity over a 12-month period, addressing the current lack of long-term experimental data in this field. Laboratory permeability tests were performed on sandy clayey silt samples contaminated with leachate at concentrations ranging from 5% to 25%. Microstructural and mineralogical analyses were conducted using SEM and XRD to identify the mechanisms behind observed changes. The results identify a critical threshold at 15% contamination, where soil behavior transitions from granular to cohesive characteristics. Hydraulic conductivity increases at low contamination levels (5–10%, up to 1.2 × 10⁻⁷ m/s) but decreases significantly at higher levels (4.172 × 10⁻⁸ m/s at 15%, 8.545 × 10⁻⁹ m/s at 20%). These changes are controlled by contamination level rather than exposure time, with values remaining stable throughout the 12-month period. The study provides essential parameters for landfill design and contamination assessment, demonstrating how leachate concentration affects long-term soil hydraulic properties through mineral formation and structural modification. Full article

Predicting rockfall dynamics is essential for effective risk management and mitigation in mining and civil engineering, where uncontrolled rockfalls can have serious safety implications. This study explores machine learning (ML) approaches to model rockfall behavior, using experimentally derived data to predict key parameters: translational and angular velocity, coefficient of restitution (COR), and runout distance. Rockfall behavior is complex, influenced by factors such as rock shape and release angle, which create irregular, nonlinear patterns that challenge traditional modeling techniques. Three ML models—K-Nearest Neighbors (KNNs), Perceptron, and Deep Neural Networks (DNNs)—were initially tested for predictive accuracy. This study found that the Perceptron model could not capture the nonlinear intricacies of rockfall dynamics, while DNNs, though theoretically capable of handling complexity, faced issues with overfitting and interpretability due to limited data. KNNs emerged as the most effective model, offering a balance of accuracy and interpretability by using instance-based predictions to reflect localized patterns in rockfall behavior. Each parameter was modeled individually, leveraging KNNs’ strength in handling the dataset’s unique characteristics without excessive computational requirements or extensive preprocessing. The results demonstrate that KNNs effectively predicts rockfall trajectories across diverse shapes and release angles, enhancing its practical application for safety and preventive strategies. This study contributes to the understanding of rockfall mechanics by providing an interpretable, adaptable model that meets the challenges posed by small, high-dimensional datasets and complex physical interactions. Full article

This study presents a comprehensive analysis of site effects in the highly seismic area of L’Aquila in central Italy, which has been conducted within the framework of a seismic microzonation project funded by the Abruzzo Region’s Department of Government of the Territory and Environmental Policies. The project was aimed at best practices on the management of urban and land territory for seismic risk mitigation. Through the integration of detailed geophysical and geotechnical data with numerical modeling, we provide an accurate assessment of local seismic amplification. Two-dimensional numerical simulations using the LSR 2D code were performed on many representative geological sections to compute amplification factors for various period ranges. This case study allowed us to outline some key considerations for best practices in local seismic response analysis and seismic microzonation studies in Italy. Given the prevalence of 2D basin edge, buried morphology, and topographic effects in Plio-Quaternary geologically complex intermontane basins in central Italy, as demonstrated in the L’Aquila case study, use of two-dimensional models is suggested. In order to validate the numerical models and their associated spectra and amplification factors, it is also suggested to compare transfer functions with HVSR microtremor measurements at control points along the studied sections. Full article

The increasing global demand for renewable energy has resulted in a high interest in wind power, with offshore wind farms offering better performance than onshore installations. Coastal nations are thus, actively developing offshore wind turbines, where monopiles are the predominant foundation type. Despite their widespread use, the effects of monopile installation methods on the overall foundation behaviour are not sufficiently yet understood. This study investigates how different pile installation procedures—jacked and impact-driven—affect the lateral capacity of monopile foundations under both monotonic and dynamic lateral loads, by comparing them with wished-in-place monopiles, the usual assumption in design, for which no soil disturbance due to installation is considered. Three finite element 3D models were employed to simulate these cases, i.e., wished-in-place monopile, jacked, and impact-driven pile, incorporating soil zoning in the latter cases to replicate the effects of the installation methods. Comparisons between all these models, when subject to lateral monotonic and cyclic loads, are presented and discussed in terms of displacements in the soil and horizontal normal stresses. Results reveal that these installation methods significantly influence soil reactions, impacting the lateral performance of monopiles under both monotonic and dynamic conditions. The impact-driven pile demonstrated the most significant influence on the monopile behaviour. These findings highlight the need for engineers to account for installation effects in the design of monopile foundations to enhance performance and reliability, as well as the optimisation of their design. Full article

A new method for measuring internal pore water pressure (PWP) is introduced to determine the critical state line (CSL) in partially frozen sand, investigating the influence of temperature and strain rate on the critical state parameters. A series of consolidated undrained and drained triaxial tests, along with internal PWP measurements, were conducted on both dense and loose specimens under different temperatures and strain rates. Similarly to unfrozen sand, a unique CSL was established for the partially frozen sand at −3 °C in both stress ($q$-$p^{\prime }$) and void ratio $(e$-$p^{\prime })$ space. The results show that the critical state friction angle (${\phi }_{cs}^{\prime }$) is not affected by temperature (warmer than −5 °C) and strain rate, while the critical state cohesion ($c_{cs}^{\prime }$) varies with temperature, strain rate and failure mode. The $c_{cs}^{\prime }$ increases with decreasing temperature from 23 °C to −3 °C and to −10 °C, but decreases to zero when the strain rate was reduced from 1%/min to 0.1%/min. In $e$-$p^{\prime }$ space, the slope of CSL could be associated with the dilation of partially frozen sand, which increases with decreasing temperature and increasing strain rate, potentially due to the increased contact area between the pore ice and sand grains. Full article

Enhancing the engineering properties of clayey soils is crucial for improving their performance in construction projects. Determining the optimal lime dosage using the Chemical Fixation Point (CFP) concept presents challenges due to soil variability, interactions with chemical and organic components, and limitations in environmental or equipment conditions, especially in pH-based methods. These challenges are exacerbated when non-standard lime or lime residues replace conventional lime. This study highlights the plasticity limit as a key parameter for optimizing lime dosage and assessing treatment effectiveness with lime residues. By analyzing four lean clay soils through CFP tests, plasticity limit measurements, and resistance evaluations, an improved methodology for CFP determination using pH–dosage curves is proposed. The findings validate the feasibility of lime residues, emphasize the plasticity limit’s critical role in lean clay treatment, and extend its relevance to soil stabilization. This work enhances CFP test accuracy and supports sustainable, adaptable soil improvement strategies. Full article

This study investigates the impact of sand-induced ballast fouling on railway track performance, focusing on track stiffness (modulus), settlement, and overall degradation. The research utilized an 18-cubic-foot ballast box designed to replicate real-world track conditions under controlled laboratory settings. A key focus was quantifying voids within clean ballast to establish baseline characteristics, which provided a foundation for evaluating the effects of sand fouling. Two distinct test series were conducted to comprehensively analyze track behavior. The first series investigated pre-existing fouling by thoroughly mixing sand into the ballast to achieve uniform fouling levels. The second series simulated natural fouling processes by progressively adding sand from the top of the ballast layer, mimicking real-world conditions such as those in sandy environments. These methodologies allowed for detailed analysis of changes in track stiffness, deflection, and settlement under varying fouling levels. The findings demonstrate a direct correlation between increasing sand fouling levels and heightened track stiffness and settlement. Dynamic load testing revealed that as void spaces were filled with sand, the track’s flexibility and drainage capacity was significantly compromised, leading to accelerated degradation of track geometry. Settlement patterns and deflection data provided critical insights into how fouling adversely affects track performance. These results contribute significantly to understanding the broader implications of sand-induced fouling on track degradation, offering valuable insights for railway maintenance and design improvements. By integrating void analysis, test series data, and load-deflection relationships, this study provides actionable recommendations for enhancing railway infrastructure resilience and optimizing maintenance strategies in sandy terrains. Full article

Designing petroleum tanks that are inter-related with pipelines in a founding position containing very compressible soil are a challenging task, particularly when a possible high-water table and considerably high seismicity are also present. Some of the issues that occur are the problems of the time dependence of settlement behavior as well as the earthquake response of the soil–structure interaction system. This work intends to portray the following: (a) an estimation of the foundations and the corresponding consolidation time response and (b) the earthquake-induced geohazard as well as the soil–structure interaction considered for the foundation of the tanks alongside pipelines in a suburban area in Greece, namely Kalochori of Thessaloniki. A numerical analysis considering the dynamic and static behavior and inter-relation among the soil mass and the buried pipeline alongside the system of the foundation type and the soil beneath is performed. Also, the foundation type that was finally chosen as the optimized solution, namely the set of gravel piles and the subsequent prestress loading, is presented and discussed. Taking into account that the soil in the vicinity of the foundation will have its shear strength during an earthquake decreased to almost zero, risk reduction actions may be suggested for large stresses imposed on the pipeline. The methodology for the dimensionality of the gravel pile group as well as the preloading session indicate that a substantial reduction in the displacements of more than 50% is obtained with the combination of the methods, something that would not be feasible if the methods were implemented individually. Full article

In deep foundation pit engineering, the soil undergoes a complex stress path, encompassing both loading and unloading phases. The Shanghai model, an advanced constitutive model, effectively accounts for the soil’s deformation characteristics under these varied stress paths, which is essential for accurately predicting the horizontal displacement and surface settlement of the foundation pit’s enclosure structure. This model comprises eight material parameters, three initial state parameters, and one small-strain parameter. Despite its sophistication, there is a scarcity of numerical studies exploring the correlation between these parameters and the deformation patterns in foundation pit engineering. This paper initially establishes the superiority of the Shanghai model in ultra-deep circular vertical shaft foundation pit engineering by examining a case study of a nursery circular ultra-deep vertical shaft foundation pit, which is part of the Suzhou River section’s deep drainage and storage pipeline system pilot project in Shanghai. Subsequently, utilizing an idealized foundation pit engineering model, a comprehensive sensitivity analysis of the Shanghai model’s multi-parameter values across their full range was performed using orthogonal experiments. The findings revealed that the parameter most sensitive to the lateral displacement of the underground continuous wall was κ, with an increase in κ leading to a corresponding increase in displacement. Similarly, the parameter most sensitive to surface subsidence outside the pit was λ, with an increase in λ resulting in greater subsidence. Lastly, the parameter most sensitive to soil uplift at the bottom of the pit was also κ, with an increase in κ leading to more significant uplift. Full article

Sub-surface soil hydrological characterization is one of the challenging tasks for engineers and soil scientists, especially the complex hydrological processes that combine key variables such as soil moisture, matric suction, and soil temperature. The ability to infer these variables through a singular measurable soil property, soil resistivity, can potentially improve sub-surface characterization. This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. Different types of sensors were installed at multiple depths in the ET covers, and resistivity tests were conducted periodically at the same location. Cross-validation and feature selection methods were used to optimize model performance and identify key variables that most significantly impact soil resistivity. Strong inverse correlations between soil moisture and resistivity (r = −0.88) and weak positive correlations with temperature (r = 0.41) and suction (r = 0.34) were observed. Among the machine learning models evaluated, artificial neural networks and support vector machines demonstrated superior predictive performance, achieving a coefficient of determination (R²) above 0.77 and lower root mean square error (RMSE) values (less than 0.14). Linear regression and decision tree models exhibited suboptimal performance because of their limitations in capturing non-linear relationships and overfitting, respectively. Random forest demonstrated superior generalization capabilities compared to decision trees; however, it encountered challenges with mid-range data variability. The findings demonstrate the effectiveness of artificial neural networks in predicting field-scale soil resistivity by utilizing hydrological variables. Full article