Geotechnics, Volume 5, Issue 2 (June 2025) – 12 articles

The geometry of rock mass fractures is typically characterized through geological and geotechnical investigations. Detailed descriptions of granitic host rock can yield valuable data for constructing fracture network models. However, significant discrepancies often arise between data representing the mechanical and hydraulic properties of rocks. At the study site, fracture geometry data were gathered through surface and underground surveying, borehole logging, and underground mapping. Three-dimensional photogrammetry was utilized alongside traditional rock mass classification methods (Q-system, RMR, GSI) to derive key parameters of fracture networks, such as orientation, size, and intensity. This study focuses on Rock Quality Designation (RQD), a measure of fracture density derived from tunnel face mapping. Findings indicate that variations in fracture frequency are significantly affected by how fracture sets are defined and by the orientation distribution of fractures. Furthermore, using the D parameter (the 2D fractal dimension of fracture frequency) as a validation measure for RQD may lead to misleading interpretations if it aggregates fracture sets on the tunnel scale. Full article

Soil carbon remote sensing has become a popular topic amongst scientists, policy makers, landholders, and others in recent years, as pragmatic perspectives on climate change, land productivity, and food security become increasingly important. Unfortunately, more than fifty years of existing research has not provided clarity or consensus on the best soil carbon remote sensing methods. A reliable, widely applicable, robust, and cost-effective means of soil carbon modelling remains elusive. As evidenced by aggregated data from 259 papers and 503 models published since 1969, much experimentation has been undertaken and soil carbon remote sensing shows promise, but the situation remains unresolved. First, this review and meta-analysis shows that soil carbon remote sensing model accuracy (via Pearson’s correlation coefficient R²) has decreased on average since 1969, and more rapidly since the year 2000. Second, the model R² does not correlate strongly with the spatial (airborne platforms compared with satellite platforms) or spectral (multispectral compared with hyperspectral) resolution of data. Third, no significant relationship between the model R² and the number of samples included in the training/test dataset is apparent. Fourth, the R² of non-parametric models (mean R² in 2022 = 0.58, n = 117) has declined more rapidly (decrease of 1.3% per year) since 1969 (mean R² in 1969 = 0.74, n = 1) than the R² of parametric models (decrease of 0.4% per year), suggesting that the algorithm applied during soil carbon modelling may be of importance. Finally, data compiled in this meta-analysis demonstrate a correlation between declining model R² and the increased use of satellite multispectral data and non-parametric algorithms, particularly machine learning, since the year 2000. There is no other evidence to suggest that prediction models prepared with multispectral data perform worse than other models, however. Hence, for the purpose of experimentation, it may be valuable to continue experimenting with the use of machine learning models for soil carbon prediction. However, when model performance is the priority, it is recommended that simple, parametric models (such as linear regression) are applied. Full article

Subsurface characterisation is a fundamental aspect of the planning and design of hydroelectric projects, as it enables the assessment of the technical and geotechnical feasibility of the proposed infrastructure, ensuring its stability and functionality. This study focuses on the characterisation of rock masses from boreholes in the “Santa Rosa” and “El Rosario” areas, located in Morona Santiago, Ecuador, to determine key parameters for the design of hydroelectric projects. Field and laboratory tests were conducted, including uniaxial compression tests, indirect tensile–Brazilian tests, point load tests, tilt tests, and geomechanical classifications using the RMR and Q systems. The results show that igneous rocks, such as basalt and andesite, exhibit mechanical properties ranging from moderate to high, with uniaxial compressive strengths exceeding 120 MPa in the case of basalt, classifying it as a strong rock. In contrast, metamorphic rocks, such as chert, exhibit lower strength, with values ranging between 69.69 MPa and 90.63 MPa, classifying them as moderately strong. The RMR and Q index values indicate a variable rock mass quality, ranging from excellent in diorite and granite sectors to low in areas with significant discontinuities and alterations. Additionally, variations in basic friction angles were identified, ranging from 18° to 38°, which directly influence the stability of the proposed structures. In conclusion, this study highlights the importance of geomechanical characterisation in ensuring the technical feasibility of hydroelectric projects, providing key information for the design and development of safe and sustainable infrastructure in the region. Full article

This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design. Full article

The breaching of river levees can have dramatic economic and human impacts. In many countries, including France, laws and regulations require the assessment and inspection of hydraulic structures. Methods are required to carry out these missions. The following article presents a method for assessing the impacts of tree vegetation on the resistance of river levees to internal erosion. Indeed, the presence of trees—particularly following the decomposition of their roots—may cause damage in the structure through contact erosion, concentrated erosion, backward erosion or suffusion. The proposed method takes into account the possible presence of trees and especially roots in different parts of the levee. The method is based on the formalization and aggregation of expert knowledge. It permits the calculation of a performance indicator, which is obtained by aggregating criteria determined using formalized status indicators. The entire method is available in the article. The method was tested on two real cases. Full article

Geotechnical centrifuge modeling is a powerful tool for investigating the behavior of geo-structural systems under realistic stress conditions. To accurately replicate the radial nature of the centrifugal acceleration field, the model surface is often curved—a detail that can significantly influence soil response. This study explores the effectiveness and limitations of incorporating surface curvature in centrifuge models through a series of nonlinear finite element analyses, utilizing an advanced constitutive model for liquefiable soils. Focusing on mildly sloping ground, the numerical models are carefully calibrated and verified for convergence to ensure accurate simulation of soil cyclic behavior. The analysis reveals that neglecting surface curvature can lead to artificially dilative responses and underestimation of liquefaction-induced lateral spreading. By modeling several centrifuge experiments under varied scaling conditions, we demonstrate that including surface curvature yields pore pressure and deformation patterns more consistent with full-scale, gravity-driven responses. These findings underscore the critical role of geometric accuracy in both physical and numerical centrifuge modeling of seismic soil behavior. Full article

In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially in complex geological settings. This study explores how the pre-processing of measurement while drilling (MWD) data impacts the accuracy of ML models. In this work, a correlational analysis of the MWD data is used as the main pre-processing procedure. For each drilling event (single borehole), correlation coefficients are calculated and then supplied as inputs to the ML model. It is shown that the ML model’s accuracy improves when the correlation coefficients are used as inputs to the ML models. It is observed that datasets made from correlation coefficients help ML models to obtain higher generalization skills and robustness. The informational content of datasets after different pre-processing routines is compared, and it is shown that the correlation coefficient dataset retains information from the original MWD data. Full article

Since the 1960s, cyclic triaxial tests have been utilized to assess the liquefaction susceptibility of cohesionless soils. While standardized procedures exist for conducting cyclic triaxial tests, there remains no universally accepted criterion for defining liquefaction in a laboratory test. The selection of a liquefaction criterion significantly impacts the interpretation of the test results and subsequent analyses. To quantify these effects, more than 250 cyclic triaxial tests were evaluated using both stress-based and strain-based liquefaction criteria. The analyses performed focused on two aspects of the liquefaction behavior: the number of cycles of loading required to initiate liquefaction and the amount of normalized dissipated energy per unit volume that must be absorbed into the specimen in order for it to liquefy. The findings indicate that for soils susceptible to flow liquefaction failures, the number of loading cycles required to induce liquefaction decreases. They also show that the amount of energy dissipation required to trigger liquefaction remains largely consistent across different failure criteria. However, for soils prone to cyclic mobility failures, both the number of loading cycles and the amount of dissipated energy required to cause liquefaction were found to vary significantly depending on the failure criterion applied. Full article

Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensitivity of the SS-HSM parameters. An automated framework was developed, and 100 finite-element (FE) models were generated, representing realistic input ranges and inter-parameter relationships. The resulting distribution of predicted surface settlements resembled observed outcomes, exhibiting a tightly clustered majority of small displacements (less than 20 mm) alongside a minority of widely scattered large displacements. Subsequently, machine-learning (ML) techniques were applied to enhance data interpretation and assess predictive capability. Regression models were used to predict final surface settlements based on partial excavation stages, highlighting the potential for improved decision-making during staged excavation projects. The regression models achieved only moderate accuracy, reflecting the challenges of precise displacement prediction. In contrast, binary classification models effectively distinguished between small displacements and large displacements. Arguably, classification models offer a more attainable approach that better aligns with geotechnical engineering practice, where identifying favorable and adverse geotechnical conditions is more critical than precise predictions. Full article

For reliable seismic design of earth-retaining structures, it is critical to accurately assess the magnitude and distribution of dynamic earth pressures. Over the years, numerous experimental and numerical studies have sought to clarify the complex soil–structure interactions in backfill–wall systems under seismic loads. This article expands on an earlier review by the authors of analytical and field performance studies addressing the seismic behavior of retaining walls. Despite extensive research, there is still no consensus on a standardized seismic evaluation method or on the necessity of including seismic loads in the design of retaining structures. This review critically examines notable experimental and numerical findings on dynamic lateral earth pressure, highlighting that the current design practices cannot be generally applied to all types of retaining structures. More importantly, these practices often rely on experimental data extrapolated beyond their original applicability. Full article

The influence of local site conditions is important when assessing the distribution of building damage and seismic risk. The average shear-wave velocity of the top 30 m of soil, V_s30, is one of the most commonly used parameters to characterize site conditions. Topographic slope is one of the proxies used to estimate V_s30 and is often used as a preliminary estimate of site conditions since a dataset is available worldwide at a resolution of 30 arc-seconds. This paper first proposes to compare the accuracy of V_s30 derived from topographic slope against detailed V_s30 zonation in five regions of the province of Quebec, Canada. A general underestimation of V_s30 is observed and site class agreement varies between 18 and 36% across the regions. Secondly, an approach is proposed to improve regional estimates of V_s30 where detailed site characteristics are not available other than the local topography and surface geology information. The surface deposit types from the geological map of Quebec are compared to V_s30 data previously obtained for zonation maps of Montreal, Saguenay and Gatineau in order to estimate V_s30 as a function of sediment deposit types as an alternative to the slope approach. A site class map for the province of Quebec is then proposed. Full article

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