Search Results (58)

Microbially Induced Carbonate Precipitation (MICP) and Enzyme-Induced Carbonate Precipitation (EICP) have emerged as promising sustainable alternatives to conventional ground improvement techniques. This paper presents a focused review of Cone Penetration Test (CPT)-based assessment of bio-cemented soils, synthesizing findings from studies spanning laboratory column tests, centrifuge models, and field trials. The review examines how CPT measurements, including tip resistance (q_c), sleeve friction (f_s), and pore pressure response (u), reflect the cementation mechanisms, treatment heterogeneity, soil-type effects, and scale dependency characteristic of MICP and EICP treatments. Key findings indicate that MICP and EICP produce distinct CPT responses: MICP-treated sands generally show stronger cementation-related stiffness signatures and more persistent improvement, whereas EICP-treated soils more commonly exhibit sharper near-surface q_c gains that may be more susceptible to reduction with time. However, long-term field CPT evidence for EICP durability remains limited. CPT interpretation is more uncertain in fine-grained and heterogeneous soils, where low permeability, preferential flow, localized cementation, and penetration-induced disturbance can produce irregular profiles that are difficult to interpret from q_c alone. Fundamental limitations of conventional q_c-based CPT interpretation in bio-cemented ground are identified, including its inability to decouple cementation effects from density, stress state, and environmental variability. Multi-sensor CPT platforms integrating shear-wave velocity probes, acoustic emission monitoring, and geochemical sensors are identified as the most promising pathway toward reliable characterization. Three priority developments are outlined: standardized CPT interpretation protocols with calibrated conversion functions for major soil types, validated multi-sensor platforms deployable under field conditions, and machine-learning tools for spatial treatment quality assessment. This review provides a structured CPT-based synthesis of bio-cemented ground and establishes an interpretive basis for future standardized assessment protocols in geotechnical practice. Full article

This study investigates marine sand collected from the southeastern coast of China through laboratory calibration chamber model tests under varying relative densities and consolidation stresses. The consolidation characteristics and cone penetration test (CPT) penetration response of soil specimens were examined, and interpretation models relating CPT parameters to soil unit weight, relative density, and shear wave velocity were established. Results show that shear wave velocity increases with relative density, with consolidation exerting a stronger enhancement. Lateral earth pressure exhibits a pronounced distance attenuation effect, with stress differences most prominent near-field and diminishing with distance. Cone tip resistance increases with both relative density and consolidation stress, with consolidation stress exerting a more significant influence on low-density specimens; sleeve friction increases linearly with relative density. The interpretation models achieve good correlations with unit weight (R² = 0.78), normalized cone tip resistance with the square of relative density (R² = 0.72), and shear wave velocity (R² = 0.85), and field validation confirms higher prediction accuracy than conventional empirical formulas for terrigenous sands. The models enable rapid, cost-effective parameter estimation from routine CPT data, though they remain site-specific, being based on nine chamber tests and validated against six field layers from a single site. Full article

Predicting the settlement behavior of jet-grout column groups in reclaimed coastal areas remains a significant geotechnical challenge, as conventional models do not capture the complex interaction between isolated stiff columns and the compliance of the composite system under wide-area loading. This study presents a field-calibrated analytical approach that reconciles single-column mechanics with full-scale group performance at a port terminal founded on highly compressible, liquefaction-prone marine backfill improved by 800 mm jet-grout columns. An extensive field-testing program—including cone penetration tests (CPTs), single-column load tests (SCLTs), and surface loading tests (SLTs)—was conducted. SCLT results revealed an elastic modulus exceeding 10 GPa, and CPT data confirmed up to a 250% increase in inter-column soil tip resistance. However, SLTs under an 85 kPa operational load yielded a back-calculated system stiffness of approximately 105 MPa, which is drastically lower than the theoretical unit cell prediction of 933 MPa. This empirical relation demonstrates that unit cell models fundamentally overestimate jet-grout group stiffness. Rather than proposing a site-specific static mobilization factor (β ≈ 0.11), this study introduces a novel, adaptive methodology. By systematically integrating single-column rigidity, group interaction, and stress transfer mechanics into untreated soil, this framework establishes a robust paradigm for accurately predicting composite stiffness and settlements across diverse geotechnical conditions. Full article

3D printing (3DP) of cement-based systems (CBSs) is a highly demanded technology in the construction field. Material requirements include specific rheological conditions for proper extrusion, followed by fast stiffening and strength gain to allow the construction process to continue, taking into account variable environmental conditions if the construction is on-site. To guarantee quality control of the process, it is essential to define field-oriented testing methodologies that allow real-time monitoring of mechanical properties’ evolution of the printed material, which will govern construction speed. This study evaluates the cone penetration test (CPT) method as a field-oriented test method to estimate the mechanical properties of 3DP CBSs over time. CPT penetration depth measurements were used to calculate shear yield stress and fresh compressive strength over time for 90 min. The experimental results were compared to two widely used laboratory tests: the fresh compressive strength test (squeeze test—SQT) and DSR test (vane test—VT). CBS pastes with and without fly ash and with three inorganic modifiers (nanoclays) and two types of organic rheology-modifying admixtures were considered. The results showed that CPT is highly conditioned by the stiffness of the paste, measured by the compressive Young Modulus (E), overestimating CBSs’ strength. The increase in E over time showed an inflection point at 130 kPa, corresponding to the evolution from plastic to pseudo-rigid behavior in the pastes. The corresponding time was used to define a linear adjustment for the average strength calculated using the CPT regarding both the fresh compressive SQT and shear yield stress VT. Full article

This study presents a validated numerical investigation into the seismic liquefaction potential of fine-grained reclaimed sediments commonly encountered in coastal, containment, and reclamation projects. Fine-grained reclaimed sediments pose a particular challenge for seismic liquefaction assessment due to their low permeability, high fines content, and complex cyclic response under earthquake loading. A fully coupled, nonlinear finite element model was developed using the Pressure-Dependent Multi-Yield (PDMY) constitutive framework, calibrated against laboratory Cyclic Direct Simple Shear (CDSS) tests and verified using in situ Cone Penetration Tests with pore pressure measurement (CPTu). The model effectively captured the dynamic response of saturated sediments, including excess pore pressure generation, cyclic mobility, and post-liquefaction behavior, under three earthquake ground motions: Livermore, Chi-Chi, and Loma Prieta. Results showed that near-surface layers (0–2.3 m) experienced full liquefaction within two to three cycles, with excess pore pressure ratios (R_u) approaching 1.0 and peak pressures closely matching laboratory data with less than 10% deviation. The numerical approach revealed that traditional CPT-based cyclic resistance methods underestimated liquefaction susceptibility in intermediate layers due to limitations in accounting for pore pressure redistribution, evolving permeability, and seismic amplification effects. In contrast, the finite element model captured progressive strength degradation, revealing strength gain in deeper layers due to consolidation, while upper zones remained vulnerable due to low confinement and resonance effects. A critical threshold of R_u ≈ 0.8 was identified as the onset of rapid shear strength loss. The findings confirm the advantage of advanced numerical modeling over empirical methods in capturing the complex cyclic behavior of reclaimed sediments and support the adoption of performance-based seismic design for such geotechnically sensitive environments. Full article

Establishing a correlation between the Cone Penetration Test (CPT) and the Standard Penetration Test (SPT) is of significant importance for geotechnical engineering practice. A novel correlation between CPT-q_t and SPT-N₆₀ based on a genetic algorithm (GA) and linear regression was proposed in this study. Based on the soil behavior type index (I_c), a GA was first applied to divide the dataset into different I_c intervals. Subsequently, linear regression was performed separately for the data in each interval to establish a correlation between CPT-q_t and SPT-N₆₀. Concurrently, the Segmented Information Criterion (SIC) was introduced to perform dual-objective optimization of complexity and prediction accuracy. The results indicate that the proposed model achieved an R² of 0.60 and an RMSE of merely 8.10. Specifically, the R² values improved by 33% and 5% compared to the traditional models and the AI models, respectively. On the validation dataset, the proposed model achieved an R² of 0.67 and an RMSE of 4.33, demonstrating higher accuracy compared to the traditional models. In summary, a method for investigating the CPT-SPT correlation is proposed in this study, characterized by simplicity, efficiency, and enhanced reliability. Additionally, a novel criterion (SIC) for mitigating overfitting is introduced. These two research findings can provide more reliable input parameters for SPT-based design, thereby supporting geotechnical engineering applications, and offer a valuable reference for relevant studies in other regions. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

The piezocone penetration test (CPTU) provides rapid, continuous measurements of in situ geotechnical parameters, making it a valuable tool for soil classification and stratigraphic identification. However, conventional classification methods frequently exhibit poor cross-regional generalizability and remain limited in achieving fine-grained stratigraphic identification. To address these limitations, this study constructs a cross-regional CPTU soil classification dataset by integrating data from three sources: the Premstaller Geotechnik database, the Global-CPT/3/1196 database, and a Chinese engineering project database. The compiled dataset was subsequently partitioned into a training set of 454,184 samples and three independent test sets. Three feature combinations and four machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), were evaluated in terms of classification performance and cross-regional robustness. Results indicate that the XGBoost-based model, using Depth, corrected cone resistance (q_t), friction ratio (R_f), pore pressure ratio (B_q), normalized friction ratio (F_r), and pore pressure (u₂) as inputs, achieved the highest performance across the three independent test sets. Misclassifications primarily occurred between adjacent soil types with similar physical characteristics. SHapley Additive exPlanations (SHAP) analysis indicated that F_r and q_t were the dominant contributors to model predictions; R_f played an important role in minority classes; Depth showed relatively balanced importance across classes, while B_q and u₂ made minimal contributions. Applying the best-performing model to unseen CPTU data and comparing the predictions with borehole logs showed that the model not only preserves overall stratigraphic trends but also identifies finer-scale stratigraphic details. Full article

In the field of geoengineering, predicting foundation settlement is a critical topic. Traditional settlement prediction methods struggle to accurately reflect settlement under complex geological conditions. This study combines cone penetration test (CPT) data and collects data from 46 different geoengineering sites from the literature. Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Support Vector Machine (SVM), and Random Forest (RF) models are individually established, and an ensemble model is proposed to predict shallow foundation settlement St. The results show that the proposed ensemble model exhibits the best predictive performance, providing a reference for practical engineering projects. The predictions of the optimal model are compared with those of single models and traditional methods, and the uncertainty of model predictions is quantified using Monte Carlo Simulation (MCS). Sensitivity analyses are conducted using feature importance analysis and SHAP methods to assess the influence of input parameters on the prediction results. Finally, Generative Adversarial Networks (GANs) are introduced to generate new data to validate the generalization capability of the model. Full article

This study presents a forensic investigation of the catastrophic failure of the La Luciana Tailings Storage Facility (TSF) in Reocín, Spain, in 1960. The collapse released approximately 300,000 m³ of tailings, causing 18 fatalities, extensive flooding of farmland and lakes, and the contamination of the Besaya River, leading to long-term environmental degradation. The analysis integrates historical documentation, cartographic evidence, in situ testing, laboratory analyses, and numerical modelling to reconstruct the failure sequence and identify its causes. Geotechnical characterization based on cone penetration tests (CPTs), shear wave velocity profiles, and laboratory testing revealed pronounced heterogeneity, with alternating contractive and dilative layers. Hydraulic analyses indicate permeabilities from 10⁻⁵ m/s in sand dam materials to 10⁻⁹ m/s in fine-grained pond deposits, with evidence of capillary saturation exceeding 20 m, favouring excess pore-pressure accumulation. Limited equilibrium and finite element analyses show that when the decant pond was within ~20 m of the dam, the factor of safety dropped to unity, triggering retrogressive flowslides consistent with field evidence. The results underline critical lessons for TSF governance: maintaining unsaturated tailings, ensuring efficient drainage and decant systems, and monitoring pond proximity to the dam. These are essential to prevent flow failures. This research also demonstrates a replicable forensic methodology applicable to other historical TSF failures, enhancing predictive models and informing modern frameworks such as the EU Directive 2006/21/EC and the Global Industry Standard on Tailings Management (GISTM). Full article

The aim of this study is to search for and verify regression models of selected geotechnical parameters of organic soils that are useful in engineering practices. Various machine learning methodologies were employed, including decision tree, ensembles of trees, support vector regression, Gaussian process, and neural networks. The work was based on two qualitatively different examples of estimating the unit weight of soil (γ_t). In the first example, the results of cone penetration test (CPT) probing (cone resistance q_c and friction resistance f_s) were used. In the second example, the results of laboratory tests of other physical properties of these soils (content of organic parts LOI_T and moisture content w) were used. The task was completed for 135 sets of test results, which were carried out at the Rzeszów training ground in Poland with in situ tests using the CPT probe and laboratory tests. A statistical analysis was carried out to initially determine the relationships between the variables. This work presents the results of a comparison of multiple linear regression models with regression models obtained using the machine learning (ML) method. The studies obtained ML models with mean absolute percentage errors (MAPE) that were smaller than those of statistical models. Consequently, for the CPT sounding data, the MAPE changed from 13.57% to 7.37%, and, for the second data set, from 7.87% to 1.25%. Software STATISTICA version 13.3 and the Regression Learner TM library from MATLAB R2024b were used to analyze the soil data. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

The shear strength and bearing characteristics of lunar soil have a strong connection with its compactness. The compactness varies significantly with depth and has an important effect on engineering activities on the lunar surface. In this study, lunar soil simulant samples of four compactness levels were prepared to explore the relationship between compactness and cone tip resistance in static cone penetration tests (CPTs). The compactness values at different depths were measured layer by layer, and CPTs were carried out. The results indicate that the cone tip resistance continuously increases with the increase in the penetration depth until it reaches a peak, and then remains constant for a certain depth. The cone tip resistance after the normalization of the overburden stress gradually increases and then decreases after reaching the peak. Models of the relationship between cone tip resistance before and after normalization and compactness were constructed using a regression algorithm. The variation in lunar soil compactness with depth can be determined by measuring cone tip resistance with this model. The research findings can provide a theoretical basis for in situ testing, site selection for lunar bases, and other related aspects on the lunar surface. 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 stability and mechanical properties of municipal solid waste (MSW) deposits in closed landfills are critical for safe land reclamation and infrastructure development. This study employs Cone Penetration Testing (CPT) to evaluate the geotechnical parameters of aged waste at three closed landfill sites in central Poland. Key parameters, including shear strength, internal friction angle, density, and liquidity index, were assessed to determine slope stability and bearing capacity for future redevelopment. Due to the heterogeneous nature of MSW, CPT results were analyzed in conjunction with empirical correlations and nomograms to improve accuracy, so the parameters can be used for future numerical modeling and proposing new computational approaches for landfill body elastic and mechanical behavior predictions. The findings indicate significant variability in landfill waste mechanical properties, influenced by waste composition, decomposition stage, and compaction history. The study highlights CPT’s reliable detremination of geotechnical parameters for landfill restoration projects, particularly for infrastructure, creating the potential for green energy and sustainable development. The results contribute to improving engineering practices in landfill restoration and ensuring the long-term stability of post-closure land use. This study also contributes to obtaining reliable results on anthropogenic waste material mechanical parameters at both the material point and at the overall structural scale, benefiting future computational methods and modeling approaches for analyzing structural and geotechnical safety of such complex and demanding structures as landfills. Full article