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Reliability analysis is necessary to address the many uncertainties, including both model and parametric uncertainties. This study systematically assesses the reliability index (β) and probability of occurrence of liquefaction (PL) using the first-order reliability method (FORM) approach on the cone penetration test (CPT) dataset, taking into account parametric uncertainties. Acknowledging the recent advancements in machine learning models and their ability to capture complex, non-linear relationships and interactions within the data, a deep learning model, namely a deep neural network (DNN), is developed and suggested based on its performance in predicting PL. We use eight statistical performance metrics to evaluate the DNN model's performance across three distinct dataset split ratios. Additional charts, such as regression plots like Taylor's diagrams, rank analysis, regression error characteristics curves, and loss and epoch curves, is provided to comprehensively assess the DNN model's performance. The current investigation demonstrates that the DNN model is promising for predicting PL on CPT datasets. Additionally, we conduct a reliability-sensitivity analysis to determine the contribution of each variable in evaluating PL. According to the sensitivity analysis, the most important parameter is the equivalent clean sand penetration resistance (qc1Ncs). It is followed by the magnitude scaling factor (MSF) and the stress reduction factor (rd). This study contributes valuable risk assessments for geotechnical engineering design and advocates for the broader integration of FORM-based ML models in liquefaction evaluation. [ABSTRACT FROM AUTHOR]

Most existing liquefaction discrimination methods statically calculate the liquefaction potential at a site based on in situ test data of liquefiable soil but ignore the fact that the pore water pressure distribution varies dynamically under actual ground motion due to the influence of sediment architecture and uneven distribution of the physical and mechanical parameters of the soil, leading to a change in the liquefaction potential of the site. In this study, taking a liquefaction site affected by the Canterbury earthquake sequence in New Zealand during 2010—2011 as an example, a 2D geological profile of a typical liquefaction site in Cashmere High School(CMHS)was established based on borehole and cone penetration test data obtained from the New Zealand Geotechnical Database. The Flac software was applied to study the influence of sediment architecture and physical and mechanical parameters on the corresponding site model. The results showed that the sandy gravel layer can effectively reduce the pore pressure accumulation in the adjacent liquefied sandy soil layer owing to its good permeability, thereby reducing the liquefaction capacity of the sandy soil layer. Soils with poor permeability, such as clay and silty clay, facilitate the accumulation of pore pressure in the adjacent liquefiable sandy soil, promoting the occurrence of liquefaction. Therefore, it is necessary to consider the influence of sediment architecture characteristics, permeability and other factors of the adjacent layers in liquefiable soil when discriminating the liquefaction potential of sandy soil. [ABSTRACT FROM AUTHOR]

The dynamic cone penetrometer (DCP) provides local soil resistance information. The difference in the vertical and horizontal data resolution (centimetric vs. multi‐metric) makes it difficult to spatialize the DCP data directly. This study uses a high‐resolution Vs$V_s$ section, extracted by the seismic surface‐wave method, as the auxiliary and physical constraint for mapping the DCP index (DCPI). Geostatistical formalism (kriging and cokriging) is used. The associated measurement error of the seismic surface‐wave data is also included in the cokriging system, that is, the cokriging with variance of measurement error (CKVME). The proposed methods are validated for the first time on a test site designed and constructed for this study, with known geotechnical perspectives. Seismic and high‐intensity DCP campaigns were performed on the test site. The results show that with decimating the number of DCP soundings, the kriging approach is no longer capable of estimating the lateral variation in the test site, and the root‐mean‐square error (RMSE) value of the kriging section is increased by 87%$87\%$. With the help of Vs$V_s$ sections constraining the lateral variability model, the RMSE values of the cokriging and the CKVME sections are increased by 25%$25\%$ and 17%$17\%$. [ABSTRACT FROM AUTHOR]

The cohesive properties of lunar regolith, combined with a low-gravity environment, result in it having a distinct mechanical behavior from sandy soil on Earth. Consequently, empirical formulas derived from cone penetration tests (CPTs) for calculating the shear strength parameters of Earth's sand cannot be directly applied to lunar regolith. This study utilized the three-dimensional discrete element method (DEM) to numerically simulate triaxial shear tests and cone penetration tests in a lunar environment. The particle contact model for lunar regolith in the discrete element method (DEM) simulation incorporated the hysteresis effect of van der Waals forces, thereby simulating the cohesive properties of lunar regolith in a lunar environment. We proposed a relationship for calculating the shear strength index of lunar regolith based on normalized cone tip resistance using the results from triaxial and CPT simulations and referencing empirical formulas derived from ground-based CPT data. The results of this study provide a valuable reference for future lunar CPTs. [ABSTRACT FROM AUTHOR]

The cone penetration test (CPT) is one of the most popular in situ soil characterization tools. However, the test is often difficult to conduct in soils with high penetration resistance. To resolve the problem, a rotary CPT device has recently been adopted in practice by rotating the rod to increase the penetrability, particularly in deep dense sand. This study investigates the underlying mechanism of the rotation effects from a micromechanical perspective using models based on the discrete element method. With rotation, the cone penetration resistance (qc) decreases by up to 50%, while the cone torque resistance (tc) increases gradually. These results are also used to successfully assess existing theoretical solutions. The mechanical work required during penetration is observed to keep rising as the rotational velocity increases. Microscopic variables including particle displacement and velocity field show that rotation reduces the volume of disturbed soil during penetration and drives particles to rotate horizontally, while contact force chain and contact fabric indicate that rotation increases the number of radial and tangential contacts and the corresponding contact forces, forming a lateral stable structure around the shaft, which can reduce the force transmitted to the particles below the cone, thus decreasing the vertical penetration resistance. [ABSTRACT FROM AUTHOR]

The failure zone around the pile tip varies greatly with the different failure patterns considered in research on the end-bearing capacity of piles. In an effort to improve the consideration of the size of the failure zone, a new failure pattern is proposed in the estimation of the end-bearing capacity of driven piles in sand and the failure zone is determined by zero-extension line (ZEL). Considering a failure zone limited below the pile end plane and an equivalent frictional contact condition with the equivalent frictional strength fully mobilized at the failure zone boundary, a more realistic prediction of the end-bearing capacity of piles is achieved. Reasonable values of parameters are obtained through parameter and numerical analysis. It is found that the failure zone is roughly within the range of 40° from the vertical direction. Comparison between the ultimate toe capacity predicted by the proposed method, a method directly using cone penetration test (CPT) data, and a method based on characteristic theory shows that the mixed zero-extension line method considering limited region failure has a better consistency with experimental data. [ABSTRACT FROM AUTHOR]

The utilization of cone penetration test (CPT & CPTu) results to assess the bearing capacity of deep foundations stands as a crucial application in geotechnical engineering. This study focuses on leveraging the outputs of the CPT test, considering the distinctive features of piles and the abundance of reliable information, coupled with the rapidity of the test. The CPT test outcomes can be employed both directly and indirectly to ascertain the capacity of the toe and shaft resistance of piles. In seismic conditions, applying earthquake acceleration to sensitive and liquefiable soils induces an increase in pore water pressure Δu, leading to a subsequent reduction in soil strength. Thus, investigating changes in excessive pore water pressure serves as a key dynamic load indicator in seismic scenarios. This research initially determines the bearing capacity of deep foundations through common methods using CPT data. Subsequently, key parameters influencing the development and dissipation of Δu, such as soil sensitivity (St), undrained shear strength (Su), and dimensionless parameters of pore water pressure 1 - B q and 1 - u 2 q t , are meticulously evaluated. This study proceeds to investigate the impact of these parameters on the bearing capacity of deep foundations, drawing insights from a comprehensive database encompassing CPT & CPTu data from 18 diverse sites worldwide. Comparative analysis between the proposed method and conventional approaches reveals a significant reduction in the aforementioned parameters' influence on the bearing capacity of deep foundations. Consequently, this finding underscores the necessity of incorporating such considerations in geotechnical bearing capacity calculations for projects situated on soils prone to liquefaction. [ABSTRACT FROM AUTHOR]

The soil composition in the Yellow River Subaquatic Delta is complex, and soil liquefaction is a serious threat to the safety of offshore projects. Two-dimensional soil liquefaction evaluation cannot meet the practical needs of engineering. In this paper, in-situ cone penetration tests (CPT) were used to investigate the classification and distribution of soils. Liquefaction variability prediction analysis was performed for the region by the inverse distance weighting (IDW) and full-cokriging methods using the liquefaction vulnerability index (LVI) and cone tip resistance as sample parameters. The results show that: (1) the overall soil grain size in the offshore direction is coarser than that in the far shore, with the clay content less than 18% at Station S1 in the near shore, but more than 90% at Stations S10 and S5 in the far shore; (2) using IDW to predict the liquefaction potential, which first decreases and then increases in the offshore direction, the one-dimensional reconsolidation settlement (SV1D) predicts better than the other two LVIs; (3) a full-cokriging method with normalized cone tip resistance as the main variable and sleeve resistance (fs) as the auxiliary variable is proposed, and its prediction accuracy is better than that of the ordinary kriging (OK) method. [ABSTRACT FROM AUTHOR]

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis. [ABSTRACT FROM AUTHOR]

A critical element in addressing disaster events is conducting a comprehensive study of the phenomenon caused by the disaster (earthquake). Therefore, a comprehensive study of the material from the disaster site, post-disaster handling, and disaster mitigation is needed to understand the phenomenon that occurs. This report presents preliminary results of field monitoring and surveys from several locations affected by the earthquake in Palu on September 28th, 2018, particularly in the Balaroa area and Anutapura Hospital. The purpose of this study is to evaluate the liquefaction potential of earthquake-affected areas using Electrical Resistivity Tomography (ERT) and Cone Penetration Test (CPT). The specific aspect investigated was the condition of the subsoil after the earthquake tested by ERT and CPT. The ERT test result on Trajectories in Balaroa shows the occurrence of a broken aquifer phenomenon, which causes an increase in the water content in the subsoil above it. Smear slides for petrographic investigation were created after the separation of grain sizes. Quartz, biotite, orthoclase, hornblende, plagioclase, and opaque minerals were among the minerals found. The collapse of the Anutapura Hospital building was possibly caused by the presence of local liquefaction phenomena that occurred in some areas of the hospital, which was proven by overlay between CPT and ERT results. Moreover, site investigation by CPT in the disaster area exhibits that soil in particular depth did not reach a non-liquefiable state after an earthquake, indicated by the value of safety factor, and possibly reliquefied for the next earthquake event. [ABSTRACT FROM AUTHOR]

Standard Penetration Test (SPT) and the Cone Penetration Test (CPT) are employed in-situ to evaluate soil parameters. In geotechnical engineering practice, engineers often conduct in-situ tests either SPT or CPT to delineate soil profile and evaluate soil parameters for bearing capacity analysis. Most of the geotechnical parameters are correlated with SPT instead and widely employed. Since numerous soil parameters are correlated with SPT N-values, it is very beneficial to establish a correlation between CPT data and SPT N-values. To predict the SPT-N value from CPT data across various soil types such as silty sand, sandy silt, silty clay, and lean clay, this study has developed an empirical model using gene expression programming (GEP). Also a comprehensive GEP model encompassing all soil types has been proposed. The input parameter used in the GEP models are CPT tip resistance (qc), CPT-Sleeve friction (qf), and effective overburden pressure (σvʹ). The effectiveness of the models is evaluated through the implementation of statistical tests, employing a comprehensive index OBJ, and performing parametric analysis. Moreover, to test the reliability of the proposed GEP models, CPT-SPT data pairs that were not utilized in the model generation were employed. The results of the proposed models testing indicated that the models either under-predicts the targeted value by 3–9% or over-predicts by 3–12%. The OBJ values indicate that silty clay has the highest value of 4.985, making it the weakest model, while the all-soil model achieved the lowest value of 1.656, thus being considered the most effective model. The results indicated that the suggested models are precise and exhibit a strong potential for generalization and prediction. [ABSTRACT FROM AUTHOR]

AbstractOffshore jackups are frequently employed for well workover or maintenance operations, necessitating repeated usage at the same location. Precisely defining the radius of the soil's plastic zone during pile penetration offers a theoretical foundation for establishing the minimum pile spacing required for subsequent well workover or maintenance activities. This study examines the radius of the plastic zone in the seabed of silty soil in the offshore region of the Yellow River Delta and the bearing capacity at various pile diameters following pile penetration, utilizing the Cone Penetration Test (CPT). The specific conclusions are as follows: (1) The disturbance radius of pile penetration into seabed silty soil ranges from approximately 2D to 3D; (2) Utilizing the pile bearing capacity calculation method proposed by Eslami and Fellenius, it is determined that the bearing capacity at a distance of 2.66D from the pile core is equivalent to the initial penetration bearing capacity; (3) According to the Mohr-Coulomb yield criterion theory, the plastic radius of pile penetration in seabed silty soil is calculated to be between 2.4D and 3.2D; (4) The disturbance radius derived from fitting the optimal bearing capacity curve aligns with the minimum pile spacing calculated according to the Mohr-Coulomb yield criterion theory, thereby confirming that the CPT test can accurately estimate the plastic radius of pile penetration. [ABSTRACT FROM AUTHOR]

The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data. [ABSTRACT FROM AUTHOR]

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The assessment of spatial variability in the ground through field experiments has many constraints due to non-homogenous ground conditions and lack of site investigations. For this reason, spatial variability has not been considered in typical analyses. Also, few studies have been conducted on ground spatial variability using models in the laboratory. However, it is necessary to evaluate spatial variability in the ground for more precise analysis and design of construction projects. In this study, in order to evaluate spatial variability in the ground, we performed a number of Mini-Cone Penetration Tests (CPTs) in ground models with one layer of silica sand soil and two layers of silica and weathered soils. Through the Mini-CPTs, cone penetration resistances with depth were measured at many points. Based on the data, the coefficient of variation (CV) and the correlation length (CL) were calculated to quantitatively analyze the vertical and horizontal variability in the ground models. The results showed how the spatial variability in the two ground models varied. This implies that considering spatial variability in the ground can significantly enhance the accuracy of the analysis and design of construction projects. [ABSTRACT FROM AUTHOR]

The evaluation of the load-settlement behavior of piles is crucial in meeting the strength and serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior is by conducting pile load tests. However, due to the considerable expense and time requirements of these tests, the load-transfer methods were used routinely in practice. The objective of this study is to explore the potential application of several machine learning (ML) algorithms to evaluate the load-settlement behavior of axially loaded single square precast prestressed concrete from cone penetration test (CPT) data. Several ML models such as artificial neural network (ANN), random forest (RF), and gradient boosted tree (GBT), were developed to estimate the load-settlement behavior from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and corresponding CPT data were compiled and used for the development of these ML models. The developed ANN, RF, and GBT models are evaluated based on several statistical criteria. The load-settlement curves predicted using the developed ML models were compared with the measured curves from pile load tests and the load-settlement curves predicted using the conventional load-transfer methods. The results of this study demonstrated the great potential of using ML models to predict the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that ML models outperformed the load-transfer methods. The results showed that both the GBT and ANN algorithms demonstrated to be the best-performing ML models. [ABSTRACT FROM AUTHOR]

During the last decades, liquefaction damages induced by earthquakes have underlined the importance of identifying effective soil improvement techniques for mitigation purposes. Vibratory methods, such as rammed aggregate piers, are commonly used to densify sands and silty sands, erroneously neglecting the influence of the lateral stress. This paper presents the results of a series of liquefaction mitigation case studies carried out using rammed aggregate piers in Christchurch (New Zealand), Boca de Briceño (Ecuador), and Bondeno (Italy) following the 2010–2011 Canterbury seismic sequence, the 2016 Muisne earthquake, and the 2012 Emilia seismic sequence, respectively. The availability of coupled piezocone and seismic dilatometer tests before and after treatment enabled a geotechnical characterization of the three sandy sites to be made, along with estimating the at-rest lateral earth pressure coefficient, and comparing the effectiveness of the treatment at the trial sites. Finally, the paper proposes an updated procedure for liquefaction assessment that takes into account both the increase in soil density and lateral stress produced by ground improvement. [ABSTRACT FROM AUTHOR]

In-situ tests for predicting geotechnical parameters related to shear strength, such as California Bearing Ratio (CBR), typically require drilling boreholes and manual cone driving, which can be timeconsuming, expensive, and labor-intensive. The Dynamic Cone Penetrometer Test (DCPT) has emerged as a simpler, faster, and less expensive alternative for predicting CBR and other geotechnical parameters. This systematic review uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to examine relevant literature on DCP testing for accurately predicting CBR and other geotechnical parameters. After trimming 111 relevant studies to 55, the review discusses various research questions, including the correlation between DCP and CBR, factors such as water content and seasonal variations, and recent advances in DCP testing. The review emphasizes the importance of considering unsaturated soil behavior, such as soil suction, in pavement subgrade design to ensure the longevity and quality of the pavement. The findings provide compelling evidence for the utilization of DCP testing as an essential tool for predicting geotechnical parameters and designing high-performance pavements. By demonstrating the DCPT's remarkable convenience, reliability, and cost-effectiveness in CBR and geotechnical parameter prediction, this study paves the way for significant advancements in pavement design efficiency and economic viability. [ABSTRACT FROM AUTHOR]

A limit plasticity solution for evaluating the effective stress friction angle in clays from piezocone tests was assessed and compared with the popular notion of using laboratory Atterberg limits on remoulded specimens. The results showed that the in situ piezocone-based method provided a far more accurate and robust interpretation of the friction angle values of 155 separate clays than empirical correlations with plasticity index when compared with laboratory benchmark values obtained from triaxial compression tests. For illustration, the piezocone-based method was shown successfully to capture the effective stress parameter of four clays with friction angles of 20–37°: normally consolidated kaolin clay in chamber tests; natural soft clay deposit located at Bothkennar, UK; Troll offshore clay in the North Sea; and soft plastic lacustrine deposits in Bogota, Colombia. [ABSTRACT FROM AUTHOR]

This paper aims to provide a design basis for engineering disaster prevention and mitigation by accurately describing the spatial distribution of soil liquefaction. A group of in-situ Cone Penetration Tests (CPT) was conducted to evaluate the seabed soil properties and liquefaction potential of the Yellow River Subaquatic Delta. The factor of safety against liquefaction (FSL) and liquefaction potential index (LPI) were utilized to investigate the liquefaction characteristics of seabed soils. The findings are as follows: (1) The delta is "near coarse and far fine," with sand and silt dominating the onshore direction and silt and silty clay dominating the offshore direction; (2) Silt, as a structured soil, has a maximum sensitivity of 10, and there are local liquefaction phenomena. The size of a Soil behavior type index can be used as the limit value of liquefaction discrimination in the region; (3) The FSL of the shallow layer is smaller, while that of the deep layer is generally larger. The FSL near the coast is smaller than that far from the coast, but its variation range is large, indicating that the shallow soil layer near the coast is more prone to liquefaction; (4) The LPI of the delta is less than 5, which is significantly affected by the wave cycle loads and geographical environment. Liquefaction can occur, but the probability of large-scale liquefaction is low. [ABSTRACT FROM AUTHOR]

The compaction control of earth works is an essential task in geotechnical engineering. In order to build more sustainably and to reduce project costs, fine-grained materials are more often used for embankment construction nowadays. The quality control of compacted soil layers is usually defined in terms of deformation moduli obtained from static and dynamic plate load tests or based on the degree of compaction, which is generally related to the Proctor density. Penetration tests, such as cone penetration tests (CPT), seismic flat dilatometer tests (SDMT) or dynamic probings (medium heavy dynamic probings (DPM)), show a potential for assessing the compaction along vertical profiles but no standardized quality criteria have been elaborated yet. The present work investigates the effects of different water contents and degrees of soil stabilization on results of CPT, SDMT, DPM, plate load tests and Proctor tests for an 8 m high trial embankment, characterized by a clayey to silty material. CPT and DMT results were found to strongly correlate with deformation moduli of static and dynamic plate load tests, enabling the definition of new quality criteria for compaction control. [ABSTRACT FROM AUTHOR]

Several correlations are available to determine the shear wave velocity using cone penetration test (CPT) data. Available correlations are applied for the studied region, which shows the requirement for developing a new correlation for the study area. This study uses CPT, standard penetration test (SPT), and multichannel analysis of surface wave (MASW) data to formulate correlations for predicting void ratio (e) and shear wave velocity (VS). The estimated void ratio at various depths was taken from the SPT bore log available for the site. A regression model has been formulated for predicting e from normalized cone tip resistance (Qtn). In developing the shear wave velocity prediction model, two types of cone data are used: mechanical and electrical. In the prediction model of VS, various parameters, such as cone tip resistance (qc), soil behavior type index (IC), effective stress ( σ 0 ′ ), e, and depth (z), are considered. The correlation regarding shear wave velocity gives a good prediction with both CPT cones. A cone factor (KC) is introduced in the developed correlation for predicting ‘e’. The proposed correlations allow design soil parameters to be easily obtained from cone penetration test data. [ABSTRACT FROM AUTHOR]

Assessing the constrained modulus is a critical step in calculating settlements in granular soils. This paper describes a novel concept of how the constrained modulus can be derived from seismic tests. The advantages and limitations of seismic laboratory and field tests are addressed. Based on a comprehensive review of laboratory resonant column and torsional shear tests, the most important parameters affecting the shear modulus, such as shear strain and confining stress, are defined quantitatively. Also, Poisson's ratio, which is needed to convert shear modulus to constrained modulus, is strain-dependent. An empirical relationship is presented from which the variation in the secant shear modulus with shear strain can be defined numerically within a broad strain range (10−4–10−0.5%). The tangent shear modulus was obtained by differentiating the secant shear modulus. According to the tangent modulus concept, the tangent constrained modulus is governed by the modulus number, m, and the stress exponent, j. Laboratory test results on granular soils are reviewed, based on which it is possible to estimate the modulus number during virgin loading and unloading/reloading. A correlation is proposed between the small-strain shear modulus, G0, and the modulus number, m. The modulus number can also be derived from static cone penetration tests, provided that the cone resistance is adjusted with respect to the mean effective stress. In a companion paper, the concepts presented in this paper are applied to data from an experimental site, where different types of seismic tests and cone penetration tests were performed. [ABSTRACT FROM AUTHOR]

The coefficient of lateral earth pressure at rest (K0) is a key state soil variable for the design of foundations and underground structures, characterizes in-situ stress state and soil condition. In this study, a method for the in-situ estimation of K0 using the cone penetration test (CPT) is proposed considering vertical and inclined cone resistances (qc). For this purpose, a series of laboratory CPTs in a soil chamber were conducted to obtain and characterize vertical and inclined qc values at various inclination angles (θ) and relative densities (DR). It was observed that the values of qc increased as θ increased, which was more pronounced at higher DR. Coupled Eulerian-Lagrangian (CEL) finite element analyses were performed to quantify the values of inclined qc at various cone penetration and soil conditions. Based on results from laboratory CPTs and CEL analyses, a CPT-based K0 correlation model was established, which was given as a function of vertical and inclined qc values. The model parameter for the proposed method was evaluated and quantified. The validity of the proposed method was confirmed from the comparison with case examples.

Assessing the constrained modulus is a critical step in calculating settlements in granular soils. This paper describes a novel concept of how the constrained modulus can be derived from seismic tests. The advantages and limitations of seismic laboratory and field tests are addressed. Based on a comprehensive review of laboratory resonant column and torsional shear tests, the most important parameters affecting the shear modulus, such as shear strain and confining stress, are defined quantitatively. Also, Poisson’s ratio, which is needed to convert shear modulus to constrained modulus, is strain-dependent. An empirical relationship is presented from which the variation in the secant shear modulus with shear strain can be defined numerically within a broad strain range (10−4–10−0.5%). The tangent shear modulus was obtained by differentiating the secant shear modulus. According to the tangent modulus concept, the tangent constrained modulus is governed by the modulus number, m, and the stress exponent, j. Laboratory test results on granular soils are reviewed, based on which it is possible to estimate the modulus number during virgin loading and unloading/reloading. A correlation is proposed between the small-strain shear modulus, G0, and the modulus number, m. The modulus number can also be derived from static cone penetration tests, provided that the cone resistance is adjusted with respect to the mean effective stress. In a companion paper, the concepts presented in this paper are applied to data from an experimental site, where different types of seismic tests and cone penetration tests were performed.

The cohesive properties of lunar regolith, combined with a low-gravity environment, result in it having a distinct mechanical behavior from sandy soil on Earth. Consequently, empirical formulas derived from cone penetration tests (CPTs) for calculating the shear strength parameters of Earth’s sand cannot be directly applied to lunar regolith. This study utilized the three-dimensional discrete element method (DEM) to numerically simulate triaxial shear tests and cone penetration tests in a lunar environment. The particle contact model for lunar regolith in the discrete element method (DEM) simulation incorporated the hysteresis effect of van der Waals forces, thereby simulating the cohesive properties of lunar regolith in a lunar environment. We proposed a relationship for calculating the shear strength index of lunar regolith based on normalized cone tip resistance using the results from triaxial and CPT simulations and referencing empirical formulas derived from ground-based CPT data. The results of this study provide a valuable reference for future lunar CPTs.

The failure zone around the pile tip varies greatly with the different failure patterns considered in research on the end-bearing capacity of piles. In an effort to improve the consideration of the size of the failure zone, a new failure pattern is proposed in the estimation of the end-bearing capacity of driven piles in sand and the failure zone is determined by zero-extension line (ZEL). Considering a failure zone limited below the pile end plane and an equivalent frictional contact condition with the equivalent frictional strength fully mobilized at the failure zone boundary, a more realistic prediction of the end-bearing capacity of piles is achieved. Reasonable values of parameters are obtained through parameter and numerical analysis. It is found that the failure zone is roughly within the range of 40° from the vertical direction. Comparison between the ultimate toe capacity predicted by the proposed method, a method directly using cone penetration test (CPT) data, and a method based on characteristic theory shows that the mixed zero-extension line method considering limited region failure has a better consistency with experimental data.

The standard penetration test (SPT), seismic cone penetration test (SCPT), and various in-situ seismic tests are commonly utilized for geotechnical site investigations. The investigated data via these tests are widely adopted to capture site characteristics for geotechnical engineering design. However, site characterizations vary in the above in-situ tests, which leads to uncertainties in the corresponding engineering analysis and design. To address these variabilities, this paper meticulously carried out the above-mentioned geotechnical in-situ tests with rigorous supervision at 13 selected sites in the Taipei Basin, yielding several valuable datasets. The datasets consist of digital investigation data including SPT-N, soil classification, CPT-qc and -fs, and the shear wave velocities (Vs) obtained from different measurements. We believe that these datasets will be beneficial for conducting various calibration studies for different geotechnical investigation methods and the corresponding geotechnical parameters.

Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance (q m ) and true tip resistance (q t ) data, where q m is the tip resistance recorded by the CPT in a layered profile, and q t represents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q m and q t pairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q m and q t are provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development. [ABSTRACT FROM AUTHOR]

Helical piles are used extensively for low and medium rise developments and are a popular foundation solution in cases where resistance to significant uplift loads is required. The ability to re-use helical piles and recent interest in their use as foundations in offshore wind and solar energy applications has renewed interest in improving existing design methods. This paper presents the results from field investigations in medium dense and dense sand that examine effects on the axial tension and compression capacity of varying the helix pitch, shaft diameter, advancement ratio and shaft tip geometry. The results from the 20 pile tests conducted (many of which included instrumentation) indicate low sensitivity to a range of the investigated parameters and a strong correlation of the capacity to the cone penetration test resistance. A design method based on these findings is proposed for typical helical pile geometries and is shown to provide predictions that are generally within 10 to 15% of the axial capacities reported for 30 other well documented pile tests reported in the literature. The method, referred to as UWASP-22, incorporates a simple means of estimating the load-displacement response of a helical pile as well as a formulation enabling prediction of the installation torque which is a common quality control measure for helical piles. [ABSTRACT FROM AUTHOR]

The shear-wave velocity V S a crucial parameter for determining small-strain soil stiffness characteristics and site classification. However, directly measuring V S in the field can be challenging, and requires specific equipment. As a result, researchers have conducted numerous studies on V S correlation, and extensive research has demonstrated that the results from cone penetration test (CPT) and standard penetration test (SPT) data are strongly related to the shear-wave velocity. Due to the uncertainty of the transformation model, the accuracy of the V S derived from the empirical equations are unsatisfactory. The purpose of the present paper is to propose a Bayesian framework for determining the probabilistic characteristics of V S while considering the transformation uncertainty. The Bayesian framework considers both the in-situ test data (SPT, CPT) and prior information, and the results show that the framework considering two in-situ tests accurately predicts the shear-wave velocity. There are several advantages of using the Bayesian method described in this study: (1) The Bayesian framework incorporates both the inherent uncertainty of the shear-wave velocity and the transformation uncertainty. (2) Prior information and field data can be combined to improve the accuracy of predictions. (3) In the framework, the statistical characteristics of V S can be ascertained from small samples of field test data. [ABSTRACT FROM AUTHOR]

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model's parameters for paddy field soils was further validated through a comparative analysis of the simulation test's cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p < 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p < 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m2, a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model's contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields. [ABSTRACT FROM AUTHOR]

The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.

The assessment of spatial variability in the ground through field experiments has many constraints due to non-homogenous ground conditions and lack of site investigations. For this reason, spatial variability has not been considered in typical analyses. Also, few studies have been conducted on ground spatial variability using models in the laboratory. However, it is necessary to evaluate spatial variability in the ground for more precise analysis and design of construction projects. In this study, in order to evaluate spatial variability in the ground, we performed a number of Mini-Cone Penetration Tests (CPTs) in ground models with one layer of silica sand soil and two layers of silica and weathered soils. Through the Mini-CPTs, cone penetration resistances with depth were measured at many points. Based on the data, the coefficient of variation (CV) and the correlation length (CL) were calculated to quantitatively analyze the vertical and horizontal variability in the ground models. The results showed how the spatial variability in the two ground models varied. This implies that considering spatial variability in the ground can significantly enhance the accuracy of the analysis and design of construction projects.

[Display omitted] • A novel Bayesian framework, called BayLUP, is proposed in this study. • BayLUP can consider statistical uncertainty, model error and spatial variability. • BayLUP allows for efficient CRF simulation of the liquefaction-induced settlement. Assessing the spatial variability of the liquefaction-induced settlement at a site often involves spatial interpolation based on some stochastic models (e.g., random fields). Accuracy of the spatial interpolation results highly depends on the number of testing data and statistical model parameters. Statistical uncertainty in model parameters is inevitable due to a lack of sufficient testing data. Moreover, the liquefaction-induced settlement at testing locations can be estimated from site investigation data using semi-empirical models derived from previous testing data and observations, for which the model error is unavoidable. This paper develops a novel Bayesian framework, called BayLUP, based on Kriging-based conditional random field (CRF), which, simultaneously, accounts for spatial variability, statistical uncertainty, and model error into probabilistic characterization of the liquefaction-induced settlement. The proposed approach is comprised of three steps, i.e., learning step (L-step), updating step (U-step), and predicting step (P-step), which are formulated from a Bayesian perspective and are sequentially implemented using the ancestor sampling method. It is illustrated and validated using real and simulated data. Results show that the BayLUP framework provides reasonable spatial interpolation results of the liquefaction-induced settlement based on a limited number of testing data and prior knowledge. Under the BayLUP framework, the spatial variability, model error, and statistical uncertainty are taken into account in a quantifiable and rational way without compromising the computational efficiency of CRF simulation. Ignoring the statistical uncertainty and model error might lead to underestimation of the prediction uncertainty. [ABSTRACT FROM AUTHOR]

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