Search Results (115)

Limitations of the cone penetration test, especially to accurately determine undrained shear strength (S_u) in soft soil deposits with high in situ stresses, have motivated the development of alternative devices, such as the T-bar and ball penetration tests, commonly referred to as flow penetrometers. These devices can estimate, in a single test, both the undrained shear strength (S_u) and the remolded strength (S_ur). When equipped with pore pressure sensors, they also provide valuable information on soil stratigraphy and consolidation parameters, making them versatile tools for characterizing soft soils. This study presents the development of two flow penetrometers, piezoball and piezo-T, highlighting relevant aspects of their design and calibration, followed by experimental campaigns conducted in two Brazilian clay deposits (Tubarão/SC and Sarapuí/RJ). Field tests enabled a direct comparison between the flow penetrometers and conventional methods, both in terms of S_u and S_ur. The investigation also examined the coefficient of consolidation of the soft soils. The results demonstrate good repeatability and consistent values for the bearing capacity factors (N_b and N_t) and remolded behavior (N_b-rem and N_t-rem). Regarding the performance of the pore pressure transducers, the piezoball test demonstrated good performance in pore pressure measurements and derived coefficients of consolidation. In contrast, despite the proposed design modifications, the piezo-T exhibited instability in the readings. Although the findings are derived from specific sites, the discussion is framed in light of the ranges reported internationally, highlighting potential local implications and reinforcing the need to expand robust geotechnical databases to support future applications. Full article

Rockfall hazard assessment and mitigation design relies heavily on three-dimensional trajectory modeling, in which the coefficient of restitution (COR) is a governing parameter controlling rebound, energy dissipation, and runout distance. In practice, COR values are commonly selected from generalized tables based on slope material type, introducing significant epistemic uncertainty and limiting predictive accuracy. This study presents a comparative evaluation of large-scale field rockfall experiments and 3-D numerical simulations conducted at a former aggregate quarry in Boise, Idaho, to assess the performance of tabulated restitution coefficients. Concrete blocks of controlled shape (spheres, cubes, and rectangular prisms) and mass (17–68 kg) were instrumented with inertial sensors and released from two slope configurations. High-resolution UAV-based LiDAR was used to reconstruct slope geometry, while dynamic cone penetrometer and friction tests were performed to characterize spatial variability in slope material stiffness. These data were incorporated into RocFall3 to simulate block trajectories using spatially varying COR values. Initial models assuming zero rotational velocity and tabulated COR ranges failed to reproduce observed runout distances, dispersion patterns, and modes of motion, particularly for non-spherical blocks. Incorporating field-measured initial rotational velocities significantly improved agreement between modeled and observed trajectories, by correcting the unrealistic sliding mode of motion previously observed. However, quantitative discrepancies in deposition and dispersion persisted, highlighting limitations associated with simplified slope geometry and the loss of small-scale surface features during LiDAR surface reconstruction. The results demonstrate that restitution behavior is strongly shape-dependent and that realistic initial conditions are essential for physically meaningful simulations. The findings underscore the need for site-specific, material-informed approaches to COR estimation and for improved integration of high-fidelity field data into physics-based rockfall models. Full article

This study focuses on the scale of wind and rainfall-induced soil erosion that is relevant to transportation infrastructure. To this end, an experimental approach was devised and carried out to assess the effectiveness of lignin, a biodegradable and non-toxic plant-derived biopolymer, in enhancing soil resistance to wind and rainfall-induced erosion. The experimental program included basic soil tests required for soil classification, wind and rainfall-induced erosion tests, pocket penetrometer tests to assess the near-surface soil strength, SEM, EDS scans, and FTIR spectroscopy to evaluate changes in the fabric and chemical composition of the soil treated with lignin. Additionally, the effect of lignin on the re-establishment of the vegetative cover after the construction completion was also investigated. It was found that an increased spraying rate of lignin solution increased both the near-surface strength and wind erosion resistance. Moreover, SEM scans showed that the presence of lignin provided abundant particle coating, which is a source of additional cohesive strength. However, the spraying rate had a minor effect on rainfall erosion resistance, which increased with an increase in lignin solution concentration. Finally, lignin treatment did not significantly affect the size of the vegetative cover and had a minor effect on soil nutrients. Full article

Land degradation in drylands increasingly threatens infrastructure and the performance of renewable energy (RE) systems through coupled hydro-chemo-mechanical changes in soil fabric, density, matric suction, and pore–water chemistry. A key gap is the limited integration of unsaturated soil mechanics with practical indicator sets used in engineering screening and operations. This narrative review synthesizes evidence from targeted searches of Scopus, Web of Science, and Google Scholar. Searches are complemented by key organizational reports and standards, as well as citation tracking. Priority is given to sources that report mechanisms linked to measurable indicators, thresholds, tests, or models relevant to dryland infrastructure. The synthesis uses the soil-water characteristic curve (SWCC) and hydraulic conductivity k(θ) to connect hydraulic state to strength and deformation and couples these with chemical indices, including electrical conductivity (EC), exchangeable sodium percentage (ESP), and sodium adsorption ratio (SAR). Practical diagnostics include the dynamic cone penetrometer (DCP) and California Bearing Ratio (CBR) tests, infiltration and crust-strength tests, monitoring with unmanned aerial vehicles (UAVs), geophysics, and in situ moisture and suction sensing. The contribution is an indicator-driven, practice-oriented framework linking mechanisms, monitoring, and mitigation for photovoltaic (PV), concentrating solar power (CSP), wind, transmission, and well-pad corridors. This framework is implemented by consistently linking unsaturated soil state (SWCC, k(θ), and matric suction) to degradation processes, measurable indicator/test sets, and trigger-based interventions across the review. Full article

Accurate assessment of compacted snow strength is critical for ensuring the safety and performance of snow runways in cold environments. The Russian Snow Penetrometer (RSP) is widely used in snow science and engineering due to its simplicity, portability, and capability for rapid field measurements under extreme conditions. Conversely, the California Bearing Ratio (CBR) test remains the benchmark for evaluating the load-bearing capacity of conventional granular materials but is seldom applied to snow because of logistical constraints and the material’s complex mechanical behavior. The relationship between these two pavement evaluation tools remains poorly defined. This work investigates how RSP strength indices relate to CBR measurements to determine whether the RSP can serve as a practical proxy for snow pavement load-bearing capacity. Side-by-side field measurements of snow pavement strength were collected over a 30 h period at two test section locations. Both methods captured temporal strength increases and spatial variability, with consistently higher values at the second site attributed to extended sintering. A moderate linear correlation (R² = 0.44) between RSP and CBR results supports a quantifiable relationship between the two methods. These findings begin to bridge the gap between conventional pavement testing and snow-specific strength evaluation, demonstrating the potential of the RSP for rapid assessment of snow runways. Continued data collection and analysis will refine this relationship and strengthen its applicability for operational use. Full article

Soil compaction significantly affects crop growth and yield. Traditional methods for assessing soil density are labor-intensive, time-consuming, and provide limited coverage of the entire field. This study aims to evaluate an alternative method for measuring soil density in real time during standard cultivation operations. The proposed approach involves measuring the elastic deformation of the cultivator shank using strain gauges mounted on the working element. Simultaneous measurement of two separate working elements was tested. Data were recorded in real time and used to generate a soil compaction map of the test field. Soil density measurements obtained using a vertical cone penetrometer served as a reference for comparison. Analysis of the collected data revealed a strong correlation between shank deformation and measured soil density, with a Multiple R = 0.814 and R² = 0.662. The results demonstrate that elastic deformation of the cultivator shank can reliably indicate soil compaction. The tested methodology provides a practical, real-time assessment of soil density during cultivation. It can be integrated into various plows or cultivators, enabling continuous monitoring of soil compaction without the labor and fuel demands of traditional mechanical tests. This approach offers a promising tool for precision soil management and optimizing field operations. Full article

Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as $q_{d0}$ and $q_{d1}$, yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates $q_{d0}$, $q_{d1}$, and $Z_c$ directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of $n=360$ observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W, ${\gamma }_{d,\mathrm{field}}$, and $R{C}_{\mathrm{SPC}}$), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ($R^2$, RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for $q_{d1}$ ($R^2=0.794$, RMSE $=5.866$), with XGBoost close behind ($R^2=0.773$, RMSE $=6.155$). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables (${\gamma }_{d,\mathrm{field}}$, $R{C}_{\mathrm{SPC}}$, and W) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Full article

Accurate numerical simulations of soil-tire interactions are essential for optimizing agricultural machinery to minimize soil compaction and enhance crop yield. This study developed and compared two approaches for identifying and validating parameters of a LS-Dyna soil model. The laboratory-based approach derives parameters from triaxial, consolidation, and cone penetrometer tests (CPT), while the optimization-based method refines them using in-situ CPT data via LS-OPT to better capture field variability. Simulations employing Multi-Material Arbitrary Lagrangian–Eulerian (MM-ALE), Smoothed Particle Hydrodynamics (SPH), and Hybrid-SPH methods demonstrate that Hybrid-SPH achieves the optimal balance of accuracy (2% error post-optimization) and efficiency (14-h runtime vs. 22 h for SPH). Optimized parameters improve soil–tire interaction predictions, including net traction and tire sinkage across slip ratios from −10% to 30% (e.g., sinkage of 12.5 mm vs. 11.1 mm experimental at 30% slip, with overall mean-absolute percentage error (MAPE) reduced to 3.5% for sinkage and 4.2% for traction) and rut profiles, outperforming lab-derived values. This framework highlights the value of field-calibrated optimization for sustainable agriculture, offering a cost-effective alternative to field trials for designing low-compaction equipment and reducing yield losses from soil degradation. While sandy loam soil at 0.4% moisture content was used in this study, future extensions to different soil types with varied moisture are recommended. Full article

The technique of static or dynamic probing is the most commonly used research method for characterizing soil media under in situ conditions. It allows engineers and geotechnical specialists to gain crucial insights into the soil properties, which are essential for designing foundations and other structural elements. One of the newest devices, the Panda variable energy dynamic penetrometer, has gained popularity due to its versatility and ease of use. It is widely used in Western European countries, especially in France, but remains practically unknown in Poland, where traditional methods as static and dynamic probe tests still dominate. The article presents a comprehensive analysis of field test results, including static cone penetrometer CPTu, lightweight dynamic penetrometer DPL, and the PANDA dynamic penetrometer. The aim of these tests was to evaluate the effectiveness of the Panda cone penetrometer by comparing the obtained results with those from the CPTu and the DPL. Correlational relationships obtained between the static (q_c) and dynamic (q_d) penetration resistance of the CPTu, DPL and Panda probes are presented. Full article

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

Introduced in the present study is a novel method for measuring soil density during standard tillage operations. The methodology involves the use of a strain gauge to measure the surface tensions of the plough body stem, reflecting the resistance force in the soil, through which the calculation of the density becomes feasible. The sensor is conveniently mounted above the contact area with the soil, allowing for easy replacement as required. Due to the small forces of surface deformation in the metal, a weight sensor with a capacity of 300 grams is used. The measurement process is continuous, and all plough bodies can be equipped with sensors. The results obtained demonstrate a high level of accuracy, with a Multiple R = 0.95 and R Square = 0.90 compared to tests conducted with a standard penetrometer, confirming the effectiveness and appropriateness of the proposed method. Full article

T-bar penetrometers have been widely used to measure strength parameters of marine clay in laboratory and in situ tests. However, using the deep resistance factor derived from full-flow conditions to evaluate the undrained shear strength of shallow clay layers may lead to significant underestimation. Furthermore, the deep resistance factor is inherently influenced by the strain-softening behavior of clay rather than maintaining the constant value (typically 10.5) as conventionally assumed in practice. To address this issue, large-deformation finite element (LDFE) simulations incorporating an advanced exponential strain-softening constitutive model were performed to replicate the full T-bar penetration process—from shallow embedment to deeper depths below the mudline. A series of parametric studies were conducted to examine the influence of key parameters on the resistance factor and the associated failure mechanisms during penetration. Based on numerical results, empirical formulas were derived to predict critical penetration depths for both trapped cavity formation and full-flow mechanism initiation. For penetration depths shallower than the full-flow depth, an expression for the softening correction factor was developed to calibrate the shallow resistance factor. Finally, combined with global optimization algorithms, a computer-aided back-analysis procedure was established to estimate strain-softening parameters using resistance-penetration curves from initial penetration tests in marine clay. The reliability of the back-analysis procedure was validated through extensive comparisons with a series of numerical simulation results. This procedure can be applied to the interpretation of T-bar in situ test results in soft marine clay, enabling the evaluation of its strain-softening behavior. Full article

Forestry machinery plays a key role in forest management, but its increasing weight significantly impacts soil condition. Machinery passage causes soil compaction, which alters the physical, chemical, and biological properties of the soil and affects CO₂ concentration. This study aimed to measure the impact of soil compaction on the evolution of CO₂ concentrations over three years. Research was conducted near Zvolen, Slovakia, where soil was compacted in 2020 by a forestry skidder. The intensity of compaction was evaluated using a digital penetrometer. CO₂ concentrations were measured with a Vaisala MI70 device, with 22 measurements taken post-compaction at an average interval of 52 days. Soil compaction was characterized by a derived penetration index. At a depth of 10 cm, the average penetration index was 119%, indicating a 19% increase in resistance. The highest index, 134%, was found at a depth of 3 cm. A correlation of 0.4 was found between the number of passes and CO₂ concentration at 30 cm depth, and 0.8 between penetration index and CO₂ concentration. Results showed a significant impact of forestry machinery on soil CO₂ concentration, even three years later. Therefore, operating practices should minimize machinery impact on forest soils. Full article

The progression of marine resource exploration into deepwater and ultra-deepwater regions has intensified the requirement for precise quantification of the undrained shear strength of clay. Although diverse in situ testing methodologies—including the vane shear test (VST), cone penetration test (CPT), T-bar penetration test (TPT), and ball penetration test (BPT)—are widely utilized for the assessment of clay strength, systematic discrepancies and correlations between their derived measurements remain inadequately resolved. The aim of this work is to provide a systematic comparison of strength interpretations across different in situ testing methods, with emphasis on identifying method-specific biases under varying soil behaviors. To achieve this, a unified numerical simulation framework was developed to simulate these four prevalent testing techniques, employing large-deformation finite element analysis via the Coupled Eulerian–Lagrangian (CEL) approach. The model integrates critical constitutive behaviors of marine clays, specifically strain softening and strain rate dependency, to replicate in situ shear strength evolution. Rigorous sensitivity analyses confirm the model’s robustness. The results indicate that, when the stain rate and softening effects are neglected, the resistance factors from the CPT and VST remain largely insensitive to shear strength variations. However, T-bar and ball penetrometers tend to underestimate strength by up to 15% in high-strength soils due to the incomplete development of a full-flow failure mechanism. As a result, their application in high-strength soils is not recommended. With both the strain rate and softening effects considered, the interpreted strength value S_ut from the CPT increases by 13.5% compared to cases excluding these effects, while other methods exhibit marginal decreases of 4–5%. The isolated analysis of strain softening reveals that, under identical softening parameters, the CPT demonstrates the least sensitivity to strain softening among the four methods examined, with the factor reduction ratio N_s/N₀ ranging from 0.76 to 1.00, while the other three methods range from 0.65 to 0.88. The results indicate that the CPT is well suited for strength testing in soils exhibiting pronounced softening behavior, as it reduces the influence of strain softening on the measured results. These findings provide critical insights into method-specific biases in undrained shear strength assessments, supporting a more reliable interpretation of in situ test data for deepwater geotechnical applications. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article