Geotechnical Engineering Research Articles

Editorial: Advances in understanding ground–structure interaction in modern geotechnical engineering

This work presents experimental results of cyclic simple shear tests at low effective stresses and in the small strain range, thus representative of the dominant stress path, stress and strain levels experienced by soil tested in flexible soil containers at 1-g stress conditions. The results constitute a useful database for the formulation and calibration of soil constitutive models in research works aimed at replicating soil behaviour in flexible soil containers. The measurements of soil response show clearly the importance of consideration of two types of hysteresis when replicating the mechanical response of soil at different strain levels representing the propagation of shear waves at different excitation levels. Applications of the obtained experimental results beyond earthquake geotechnical engineering, in offshore geotechnical engineering and in extraterrestrial environment, are briefly highlighted.

Abstract This study investigates the use of biopolymer stabilisation in combination with sisal fibre (SF) reinforcement to improve the dynamic shear resistance (DSR) and reduce the compressibility of Berea Red Soil (BRS). The investigation evaluates the dynamic shear strength and compressibility, known as key attributes of subgrade performance. Oedometer and dynamic shear tests were conducted on the BRS samples with varying biopolymer concentrations and fibre contents. The performance metrics, including the DSR and Coefficient of Compressibility, were analysed to evaluate improvements in the subgrade response. The reinforcement technique produced notable gains in dynamic shear resistance, with some samples demonstrating up to 86% enhancements relative to untreated BRS. This substantial increase is attributed to the enhanced resistance of SF against dynamic stress. In parallel, compressibility measurements indicated a significant reduction in soil deformability under compression loading, affirming the efficacy of the biopolymer inclusion in mitigating compressibility through particle bonding and cohesion improvement. These results underscore the potential of combining biopolymer stabilisation with sisal fibre reinforcement to develop a more durable and efficient subgrade material. The approach offers a sustainable and technically robust alternative for applications in geotechnical engineering, particularly where improved resistance to dynamic forces is requisite.

Freeze-thaw durability and environmental risk assessment of silty soil improved using EICP combined with magnesite tailings powder.

Application of geophysical methods for 2D and 3D geomechanical modeling in geotechnical engineering

The versatile, Effective Stress Beta Method can be applied to driven pile design across various soil profiles. However, it is not widely practiced, potentially due to unreliable FHWA guidance for selecting pile design coefficients (β & Nt). This study addresses this issue by evaluating FHWA design coefficients against measured data from the Deep Foundation Load Test Database (DFLTD) V.2. The investigation reveals significant disparities between measured load-carrying capacities (Qm) and capacities calculated using FHWA design coefficients (Qc). The design coefficients, β & Nt, were then systematically back-calculated from load test data and were found to differ from the FHWA design coefficients, as hypothesized. In order to improve the design coefficients selection guidance, a neural network (NN) and machine learning (ML) based approach is proposed. The models BetaSPTNet (Artificial-NN for β) and NtSPTNet (Dense-NN for Nt) outperformed other models in predicting β & Nt. The study showcases NN’s adaptability in handling ambiguous correlations such as the one between geotechnical engineering properties of soil and β & Nt design coefficients. The proposed NN architecture improves precision and reduces uncertainty in determining β and Nt using geotechnical properties derived from SPT and CPT soil exploration data. Finally, we propose a modified approach integrating the traditional Beta design method with NN-predicted design coefficients. This integration significantly enhances the accuracy of calculated pile load-carrying capacities.

The goal of soil stabilization, a crucial component of geotechnical engineering, is to increase the performance and stability of soils used in building. Conventional stabilizers, including cement and synthetic chemicals, frequently provide financial and environmental difficulties. The goal of this study was to assess gum Arabic's potential as a substitute soil stabilizer. Gum Arabic is a naturally occurring polysaccharide. The main objective is to evaluate how well gum Arabic improves the soil's consolidation qualities by ascertaining the effect of gum Arabic on soil stability and determining the best doses for various uses by incorporating it into different types of soil. Gum Arabic was applied in different dosages (0% to 1.0%) to two types of soil: brownish fine-grained clayey soil (Sample B) and greyish sandy gravelly soil (Sample A). To ascertain the impact of stabilization, the soil's shear strength (direct shear) was examined. According to the findings of this study, moderate dosages of gum Arabic considerably enhanced the qualities of the soil. The stabilization effects peaked at 0.7% for Sample A (54%, i.e 37% increase) and 0.7% for Sample B (40%, i.e 18% increase), indicating a significant increase in the soil samples' shear strength. The study's conclusion highlights the ideal treatment amount of 0.7% Gum Arabic, which increases soil strength.

This research is an investigation of the geotechnical engineering properties of the Laterite soils employed for the construction of the 9.5km Ring Road Three in Ukyo Local Government, Akwa Ibom State, Nigeria. Two samples were collected from the borrow-pit at Mokoro Nist, nit Ibim Local Government Area, Akwa Ibom State at depths ranging from 0.5m to 1.5m. The samples were subjected to the following laboratory tests: Particle (grain) size analysis, Atterberg limit test, Compaction test and California bearing ratio (CBR) test. From the above-mentioned laboratory tests, the percentage passing sieve no. 200 (75µm) ranges between 1.4501% and 4.1476%, liquid limits (LL) ranges between 27% and 29%, Plasticity index (PI) ranges between 4.13% and 6.05%, Optimum moisture content (OMC) ranges between 9.82% and 10.42%, Maximum dry density (MDD) ranges between 19.21kN/m3 and 19.33kN/m3 and the CBR (unsoaked) ranges between 61.21% and 61.46%. In accordance with clause 6201 and 6252 of the Federal Ministry of Works and Housing (1997) Specification Requirement, the lateritic soil is considered suitable for sub-base and base course since their percentage passing sieve No. 200, liquid limits, plasticity index are not greater than 35%, 50% and 12% respectively and also MDD is not greater than 20.0kN/m3 recommended. It could also be deduced that the borrow –pit material is suitable for sub-base only since the CBR (unsoaked) is not less than 30% recommended. However, the Lateritic soils have been found to be of A-2-4 based on the AASHTO classification Scheme and hence, they have significant constituent materials of mainly silty or clayey gravel and sand classified as sandy clay according to O’Flaherty. In conclusion, the borrow-pit material deployed will only be adequate and suitable for sub-base and will not be suitable for base course.

ABSTRACT In geotechnical engineering, slice methods are the most widely used computational techniques for analyzing slope stability. Notably, the Morgenstern‐Price and Janbu approaches rigorously enforce the equilibrium of forces and moments within these slice methods. However, these rigorous methods often encounter challenges with numerical convergence, particularly when using very thin slices. This study addresses these issues by building on previous research that examined the indeterminacy and numerical instability of the differential equations governing slope stability. It extends these differential models into discrete finite difference models, similar to those utilized in slice methods. Finite difference (FD) schemes are employed to construct three FD models (namely, dM, dJ, and dB models), which inherit their differential counterparts' local indeterminacy and numerical instability. Inspired by the Morgenstern‐Price method, the dM‐model and the proposed dB‐model demonstrate greater stability than the dJ‐model deriving from the Janbu method. The findings offer valuable insights into the impact of thin slices on numerical stability and enhance our understanding of the limitations inherent in slope stability analyses.

This study evaluates and optimizes empirical correlations between shear wave velocity (Vs) and cone penetration test (CPT) data for various soil types in Hungary. A comprehensive database of 914 data pairs was compiled from multiple cities with diverse geological conditions, incorporating SCPTu and MASW measurements. Over 40 existing Vs–CPT correlations were statistically assessed using parameters such as RMSE, RD, K, CVK, and RI to determine their accuracy across different soil types and depositional settings. The most promising correlations were further refined using regression analysis, leading to the development of improved models tailored for Hungarian soils. These new correlations were evaluated both graphically and statistically, showing enhanced predictive performance, particularly for coarse-grained soils. The final proposed models demonstrate significant reductions in estimation error, with RMSE improvements exceeding 35%. This work provides geotechnical engineers in Hungary with robust, site-adapted tools for seismic site characterization and supports safer and more reliable subsurface profiling practices.

To examine the propagation mechanisms of wing cracks in rock under hydro-mechanical interactions, we developed a theoretical model that incorporates compressive loading effects, T-stress, and hydraulic pressure. Complementing this, a coupled hydro-mechanical-damage numerical model was employed for observation and comparative analysis. The influences of water pressure and confining pressure on wing crack evolution were systematically investigated. When T-stress effects are considered, the initiation angle of the compression-shear crack varies with the crack’s inclination, which is consistent with previous experimental results, as opposed to the initiation angle of 70.5° from the traditional theory. In the presence of water pressure, hydraulic forces transmitted within the cracks partially counteract the com-pressive stresses on the crack faces, thereby enhancing tensile damage. This results in an increased mode I stress intensity factor at the wing crack tip in the theoretical model, promoting both crack initiation and propagation. Conversely, an increase in confining pressure elevates compressive stress while reducing shear stress along the crack planes, which delays tensile damage and decreases the mode I stress intensity factor, thus inhibiting wing crack development. The numerical model effectively visualizes both the crack propagation process and the associated flow field, with simulation outcomes demonstrating good agreement with theoretical and experimental results. These findings contribute to a deeper understanding of crack propagation and failure behavior in geotechnical engineering contexts.

Precise calculation of soil bearing capacity is critical in geotechnical engineering to ensure ground stability and structural safety. Traditional evaluation techniques such as the Standard Penetration Test (SPT), Cone Penetration Test (CPT), and Plate Load Test (PLT) are well-established but often time-consuming, labor-intensive, and spatially constrained. This study presents a semi-automated, real-time method for estimating soil bearing capacity by integrating torque, force, and rotational speed sensors into conventional drilling equipment. Unlike prior Measuring-While-Drilling (MWD) approaches, which have largely focused on granular formations and deeper borehole profiling, this work introduces a custom-built torque measurement system specifically designed for shallow-depth, low-permeability clayey soils. The system offers improved sensitivity to subtle resistance changes encountered during cohesive soil penetration, thereby enhancing prediction accuracy in scenarios where conventional MWD systems typically underperform. Laboratory and field tests were performed on four clayey soil types (CH, MH, SC, CL), and the collected drilling parameter data were analyzed using Multiple Linear Regression (MLR) and Response Surface Methodology (RSM). The MLR model explained 95.6% of the variability in soil bearing capacity (R2 = 0.956, MAPE = 7.87%), although it was limited in capturing non-linear interactions. In contrast, the RSM model accounted for 99.7% of the variability (R2 = 0.997, MAPE = 0.72%) and more effectively modeled the complex relationships among drilling parameters. Among all inputs, torque emerged as the most significant predictor of bearing capacity. The developed framework enables faster, more cost-effective, and sensor-integrated evaluation of soil strength, especially for cohesive soils offering a practical alternative to conventional testing. Future work will extend this approach to mixed and granular soils, deeper borehole conditions, and adaptive, ML-driven real-time control systems to enhance field-scale geotechnical applications.

Optimization has become a central concern in geotechnical engineering with increasing constraints on energy resources and the rising demand for cost-effective operations. Drilling, as a critical and energy-intensive component of mining and tunneling (particularly in transportation infrastructure), requires efficient and intelligent performance strategies. Monitoring While Drilling (MWD) provides a promising approach for real-time acquisition of drilling conditions. Recent advancements, including the integration of Acoustic Emission Technique (AET) with artificial intelligence (AI), enhance data-driven modeling and predictive analysis of drilling performance. In this study, vibroacoustic signals and drilling parameters were analyzed to predict penetration rate (PR) using three machine learning models: Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR). Comparative evaluation showed that all three models achieved reliable predictive accuracy, with ANN reaching R2 = 0.744, MAPE = 36.98%, RMSE = 0.161; RF yielding R2 = 0.816, MAPE = 31.54%, RMSE = 0.142; and SVR attaining R2 = 0.808, MAPE = 29.52%, RMSE = 0.141. The results demonstrate the feasibility of integrating vibroacoustic monitoring with AI-driven models for accurate PR prediction. This approach supports real-time decision-making, enhances drilling efficiency, and promotes sustainable practices in both underground and surface excavation projects.

Abstract The stabilization of expansive soils is a critical concern in geotechnical engineering, as these soils often exhibit poor mechanical properties, leading to issues such as high plasticity, low shear strength, and significant swelling and shrinkage behavior. The increasing demand for sustainable construction materials has prompted the exploration of industrial byproducts as effective soil stabilizers. This study investigates the influence of marble dust, a waste material from the marble industry, and molasses, an agricultural byproduct, on the geotechnical properties of expansive soil by conducting laboratory testing. Several key laboratory tests, including differential free swell, Atterberg limits, modified Proctor test, unconfined compressive strength, California bearing ratio, pH, and electric conductivity, were conducted to assess the effect of varying percentages of marble dust and molasses alone and in combination with each other on geotechnical characteristics of expansive soil. The results of laboratory testing revealed that the addition of marble dust (MD) and molasses (M) significantly reduced the differential free swell, liquid limit, and plasticity index of expansive soil (S) and nearly eliminated swelling potential with a combination of 15% MD and 6%. Also, a combination of S: MD:M:: 81:15:6 transformed the soil from high to low plasticity, making it suitable for subgrade applications. The maximum dry density was also found to be improved, reaching a value of 1.922 g/cc at an optimum moisture content (OMC) of 10.6% for the above combination. The unconfined compressive strength (UCS) tests showed a 124% increase in strength after 28 days, with a soaked California Bearing Ratio (CBR) of 15.28% for S: MD:M:: 81:15:6 mixture. The Scanning Electron Microscopy (SEM) analysis indicated that MD densified the soil structure, while molasses improved particle bonding. Additionally, pH and electrical conductivity tests revealed increased soil alkalinity from MD, balanced by molasses' acidity.

The Cerchar Abrasiveness Index (CAI) is a vital parameter in geotechnical engineering, especially when it comes to tunneling and mechanized excavations. The study employed a comprehensive dataset of 163 samples representing various rock types, including igneous, sedimentary, and metamorphic formations. The methodology included three base algorithms (XGBoost, LightGBM, and Random Forest), improved by three distinct metaheuristic techniques: Arithmetic Optimization Algorithm (AOA), Reptile Search Optimization (RSO), and Harris Hawks Optimization (HHO). The Brazilian tensile strength (BTS), uniaxial compressive strength (UCS), equivalent quartz content (EQC), and brittleness index (BI) were the four main rock parameters used to make the predictive models. The model was further evaluated by splitting the data into 80% training and 20% testing sets. Subsequently, the model was compared to 17 real-world hard rock TBM projects in different countries and geological conditions. The AOA-optimized versions performed nicely, with AOA-LightGBM doing the best on the held-out test set (R² = 0.952, RMSE = 0.290, MAE = 0.208, VAF = 0.952). External validation showed that AOA-XGBoost performed properly, with the highest correlation coefficient of 0.8308 compared to field measurements from international tunneling projects. Also, the AOA-XGBoost did well on tests with R² = 0.951, RMSE = 0.296, MAE = 0.223, and VAF = 0.951. Using SHAP values to examine feature importance revealed unique parameter influence signatures. EQC was the most important parameter in XGBoost models, while UCS had the greatest impact in LightGBM and Random Forest-based models. The new method described here is an important advancement in CAI prediction methodology. It is more accurate and efficient than traditional experimental testing methods, and it works well on different types of rock. Its engineering applicability has been proven through real-world operational scenarios.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22098-9.

The accurate determination of the soil-water characteristic curve (SWCC) and unsaturated hydraulic conductivity is vital across multiple disciplines, including hydrogeology, soil science and geotechnical engineering. Nevertheless, conventional techniques for measuring these unsaturated soil parameters are often laborious and time-consuming, posing significant practical challenges. This research presents a new technique for estimating SWCC and unsaturated hydraulic conductivity by employing fractal theory and utilizing a three-dimensional fractal dimension (Ds). The results revealed that all three soils exhibited fractal characteristics in their particle surfaces, with Ds values of 2.611 for Malan loess, 2.688 for paleosol, and 2.771 for remolded loess. The complexity of the pore structure was in the order of remolded loess > paleosol > Malan loess. The test results of the soil-water characteristic curve indicate that the water storage capacity of the three soils was in the order of paleosol > remolded loess > Malan loess. Compared with the Brooks-Correy fitting curve, the fractal model is feasible in predicting the soil-water characteristic curve. Two models were used to predict the unsaturated hydraulic conductivities of three types of soil, and the results were compared with the measured values. By comparing the R2 and RMSE values of the fractal model and the Brooks-Corey model, it was found that the fractal model proposed in this paper can effectively predict the unsaturated hydraulic properties of these three types of soil. This study provides a simple and effective alternative for predicting the SWCC and unsaturated hydraulic conductivity of unsaturated soils, with potential applications in various earth science fields.

The application of rubber in geotechnical engineering has gained widespread popularity due to its potential to enhance the engineering properties of foundation fills while reducing environmental pollution. This study focuses on investigating the influence of the rubber fiber content on the performance of calcareous sand by conducting a series of triaxial tests. The effects of the rubber fiber content and axial pressure on the strength, deformation, permeability, and particle breakage of rubber–calcareous sand were systematically studied. The experimental results reveal that increasing the rubber fiber content reduces the strength of rubber–calcareous sand, but it also inhibits the shear dilation and mitigates the occurrence of rupture surfaces: the sample with a rubber content of more than 10% only has shear-contraction. Both the rubber fiber content and axial stress contribute to the increased impermeability of rubber-modified calcareous sand, although they exhibit different characteristics. The relationship between the rubber fiber content and permeability coefficient is linear, while, under increasing axial stress, the permeability coefficient initially decreases rapidly; when the deviatoric stresses exceeds 1000 kPa, the decreasing rate slows down. Furthermore, rubber fiber significantly reduces particle breakage in calcareous sand. The relationship between the input energy applied to rubber-modified calcareous sand and the relative breakage rate of calcareous sand can be well-fitted with a power function. Samples with a higher rubber fiber content exhibit a lower relative breakage rate of calcareous sand under the same absorbed input energy. Through the research results of this paper, the best rubber ratio can be selected as the road filler in engineering practice to ensure both cost-effectiveness and environmental protection.

Geomembranes (GM) have been extensively used for waterproofing applications, and often they are in contact with soil materials or other geosynthetics for mechanical protection. Strength evaluation at the interface between the GM and the contact material is fundamental to ensure a good design that guarantees a low probability of this interface failure. This paper analyses and compares four Artificial Neural Network (ANN) models (varying the number of inputs and hidden layers) and the Random Forest (RF) technique to predict sand/GM interface shear strength based on 495 results from previous investigations. All models were optimized with the Differential Evolution (DE) algorithm. The Coefficient of Determination (R2) and root mean squared error (RMSE) were set as evaluation criteria for the accuracy of the developed models. The results show that RF performs best as a prediction tool for the data analysed. Data correlation and RF feature importance analysis were also conducted, establishing GM asperity height as the most significant variable for the collected data. The results show the great potential of Machine Learning applications for predicting the interface shear strength between sand and geomembranes in geotechnical engineering constructions.

Guest Editorial Message for the ‘Women in Geotechnical Engineering’ Special Issue

This study develops a discrete element model incorporating the water–ice phase transition volume effect to simulate frost damage in saturated granite. The model investigates the damage evolution and mechanical degradation under freeze–thaw cycles. The results show that during freeze–thaw cycles, the model’s temperature field exhibits non-uniform distribution characteristics and geometric dependency, with lower maximum temperature differences in Brazilian disk models versus uniaxial compression specimens. Frost heave damage progresses through three distinct stages: localized bond fractures (1~5 cycles); accelerated crack interconnection and branching (15~20 cycles); and fully interconnected damage zones (25~30 cycles). As the number of freeze–thaw cycles increases, the crack network significantly influences the mechanical behavior of the model under load. The failure mode of the loaded model undergoes a transformation from brittle penetration to ductile fragmentation. Freeze–thaw cycles cause more significant degradation in the tensile strength of granite compared to compressive strength. After 30 freeze–thaw cycles, the uniaxial compressive strength and Brazilian tensile strength decrease by 47.5% and 93.8%, respectively. These findings provide theoretical support for assessing frost heave damage in geotechnical engineering in cold regions.