Cone Penetration Test Soundings Research Articles

Discrete element modelling of rotary CPT and its applications

The cone penetration test (CPT) is a major in-situ testing with advantages in efficiency, reliability, and continuous measurement. However, it is often limited in penetration depth in soils such as dense sand and gravel due to high resistance and insufficient pushing force. Studies have shown that rotation can reduce penetration resistance, leading to the development of the rotary CPT. In addition, the torque measurement in rotary CPT could potentially provide extra information for soil behavior. This study simulates regular and rotary CPT tests with varied confining pressure and initial relative density using the discrete element method (DEM). Macroscale results verify that rotation can improve the penetrability of CPT by reducing vertical soil resistance. The reduction mechanism was explained by the microscopic observations and theoretical calculations that rotation can reduce the magnitude of the contact force and tilt its main direction horizontally. To advance the application of rotary CPT, a strong linear correlation between the measurements in rotary and regular CPTs was established, enabling interpretation of the rotary CPT measurements via the vast knowledge accumulated for regular CPT soundings. Furthermore, a theoretical derivation was developed to calculate the soil-probe interface friction coefficient directly by using the measurements of rotary CPT. The influence of the confining pressure, probe friction coefficient, and rotational speed ratio on the calculation accuracy are discussed and explained via particle-scale observations.

Dominant factors in MiniCone, CPT and pile correlations: A data‐based approach

AbstractThe cone penetration test (CPT) contributes to the design and analysis of piles regarding geometry, installation effect, and pile capacity (shaft and toe resistance). MiniCone, as an alternative to CPT sounding, has been used to carry out field and laboratory investigations by physical modeling. More tests can be practically carried out through light equipment and small soil mass, involving fewer errors caused by boundary conditions. Furthermore, it can be used for in situ testing, such as quality control, assessment of ground improvement, and subgrade characterization. A database comprising MiniCone and CPT records in field and physical modeling is proposed with a variety of cone diameters. The case study records in the database have been obtained from 140 tests compiled from data from 26 sources. The sources include the results of 20 physical modelings and field data from six sites in 10 countries. The data comprise MiniCone and CPT cone tip resistance (), and sleeve friction (). The different cones are used in sandy, silty sand, and clayey soils via simple chambers (1 g), calibration chambers, and frustum confining vessels. In addition, correlations were found in penetration records in terms of physical modeling types, cone diameters, penetration rates, and soil densities. Moreover, and are related to capacities of pile toes and shafts using proper correlation coefficients less than unity, respectively. Correlations and dominant factors in geotechnical practice between MiniCone, CPT, and pile have been reviewed and discussed.

Retrospective evaluation of large settlements of a very soft clay: Alameda, California

The Harbor Bay Isle residential project in Alameda, CA, USA, comprises some 600 acres (243 ha) reclaimed from the margin of San Francisco Bay underlain by very soft (C c ∼ 1), high-water-content clay (e 0 > 2): San Francisco Bay Mud. Settlements of as much as 2.5 m developed from about 7 m (varying across the site) of sandfill surcharge, with records over some 43 years extending through primary consolidation and into secondary consolidation (creep). Iterative numerical modelling using large-strain theory (implemented in the Microsoft Excel software program), constrained by in situ void ratios and oedometer trends, achieved close matches with settlement histories over the full 43 years for the three locations considered, each of which displayed a markedly different history from the others. The presence or absence of a dried ‘crust’ was found unimportant; instead, the proportion of subunits within the Bay Mud controlled the evolution of settlements. Subunits within Bay Mud, evident in modern cone penetration test soundings (which were not available during the reclamation works), exist across the region and have systematically different states and properties. Further, while ageing is a factor, depositional conditions of the Bay Mud have produced a residual fabric that presents as a component of the pre-consolidation pressure.

Fast stratification of geological cross-section from CPT results with missing data using multitask and modified Bayesian compressive sensing

Since cone penetration test (CPT) is reasonably rapid, affordable, and repeatable, it has been widely used in situ for subsurface soil stratification and classification in geological and geotechnical engineering practice. When used for soil stratification across a 2D geological cross-section, however, it is often observed that some CPTs probe deeper than others, and that some CPT soundings may contain missing data due to presence of gravel-sized particles or intentional bypassing of gravelly soil layers. Arguments above and frequently encountered problem of a small number of CPT soundings in practice pose a great challenge for 2D soil stratification, especially for nonstationary CPT within multilayers. While certain methods have been proposed hoping to address these concerns, they are frequently constrained by either stationary assumption of data, autocorrelation function forms, or computational issues. This study introduces a data-driven multitask Bayesian compressive sensing (MT-BCS) method to estimate missing data for CPT sounding of interest, and then develops a modified 2D BCS method for fast interpolation for horizontal locations without CPT soundings. The proposed method is demonstrated and validated using both numerical and real-world CPT data. Results show that proposed method is both efficient and robust in terms of missing data estimation in each CPT sounding and soil stratification for a 2D geological cross-section.

Cone penetration tests in saturated and unsaturated silty tailings

Laboratory-controlled cone penetration test results for two silty tailings in a variety of saturated and unsaturated states, obtained using two calibration chambers, are presented then interpreted using a state parameter-based approach. For each, the cone penetration resistances, which increase due to the presence of suction when the tailings are unsaturated, can be normalised using the initial mean effective stress to establish a relationship with the initial state parameter. The relationship is applicable to saturated and unsaturated conditions, as long as the presence of suction hardening as well as the influence of suction on the mean effective stress are accounted for, and as long as the cone penetrations occur under drained conditions. The relationships enable state parameters to be back-calculated from normalised cone penetration resistances. The state parameters enable estimations of the tailings’ peak friction angles for drained loadings as well as their propensities to liquefy during undrained loadings. Application is demonstrated using cone penetration test soundings in the tailings storages from which the samples were taken, showing how in situ void ratios and state parameters, as well as future state parameters if the tailings were to become saturated, can be determined. Close agreements with direct measurements of void ratios are shown.

Chilean liquefaction case history database

This article describes a liquefaction database that contains a summary of 209 liquefaction, non-liquefaction, and marginal case histories compiled from the 2010 Mw 8.8 Maule, 2014 Mw 8.2 Iquique, 2015 Mw 8.3 Illapel, and 2016 Mw 7.6 Melinka earthquakes, where the liquefaction phenomenon caused damage to buildings, bridges, roads, and drainage systems, generating millions in losses at the infrastructure level. The database structure is organized into nine main tables that contain site information, geotechnical tests, and seismic parameters. The main tables include the locations of the sites, surface evidence of liquefaction or absence of them, geotechnical parameters from boreholes, and geophysical and laboratory tests. The database contains 7977 m of standard penetration test logs and 390 m of cone penetration test soundings from 209 sites explored. In addition, the seismic parameters of these earthquakes include ground-motion intensity measures estimated for each site. The information in this database allows a better characterization of the seismic demand and the geotechnical properties of the soil involved in predicting liquefaction triggering in this subduction zone. The data associated with this article are available in DesignSafe, where users can freely download and process data to train or evaluate predictive liquefaction models.

A framework to predict the load-settlement behavior of shallow foundations in a range of soils from silty clays to sands using CPT records

Using a set of cone penetration test (CPT) records, the current paper develops a general framework based on regression analyses to model the load-settlement (q-s) behavior of shallow foundations resting on a variety of soils ranging from silty clays to sands. A three-parameter hyperbolic function is employed to rigorously examine the obtained q-s curves and to determine the model parameters. Also, the results of some CPT soundings, including the corrected cone tip resistance (qt) and the skin friction (Rf), are adopted to predict the results of plate load tests (PLT). The findings corroborate the high accuracy of the proposed model, the reasonable performance of the hyperbolic function and the use of the Volterra series to predict the q-s curves. Moreover, the obtained curves from the newly developed model are compared to those from other methods in the literature which cross-confirms the efficacy of the current model. A sensitivity analysis is also conducted, and the exclusive effects of all the contributing parameters are assessed among which Rf is shown to be the most influential. Ultimately, simple solutions are adopted to determine various key geotechnical parameters, like the ultimate bearing capacity (qult), the allowable bearing capacity (qa) and the modulus of subgrade reaction (ks).

CPT-based probabilistic liquefaction assessment considering soil spatial variability, interpolation uncertainty and model uncertainty

In engineering practice, simplified procedure based on cone penetration test (CPT) results is widely used for evaluating soil liquefaction potential. Since the CPT-based simplified procedure was developed from observations during past earthquakes, it is semi-empirical and involves significant uncertainty (e.g., model uncertainty). In addition, due to time, budget and access constraints, CPTs are often sparsely conducted at a specific site, leading to a significant uncertainty associated with interpolation of the limited CPT soundings, particularly along horizontal direction. Furthermore, it is well-recognized that spatial variability of soil properties has a remarkable effect on soil liquefaction. All these variability and uncertainties greatly affect the seismic liquefaction assessment results, particularly spatial distribution of liquefiable soils in a site, and liquefaction-induced damage. This underscores a question of how to properly incorporate these variability and uncertainties in liquefaction assessment, e.g., how to characterize spatial distribution of soil liquefaction potential in a site with quantitative consideration of the abovementioned variability and uncertainty. To address this issue, this paper develops a novel probabilistic method for characterizing spatial distribution of soil liquefaction potential through factor of safety, FS against liquefaction in a vertical cross-section using Bayesian compressive sampling and Monte Carlo simulation. Using the proposed method, many random field samples of FS cross-section are obtained directly from limited CPT measurements. The proposed method is illustrated using both a simulated data example and a set of real CPT data from Christchurch, New Zealand. It is shown that the proposed method performs well and provides reasonable liquefaction assessment results.

Unsupervised and Simultaneous Stratigraphic Interpretation of CPT Soundings at Site Scale

Abstract This paper presents a novel Bayesian machine-learning approach for unsupervised and simultaneous soil stratigraphic interpretation of cone penetration test (CPT) soundings at the site scal...

Delineating the spatial distribution of soil liquefaction potential in a cross-section from limited cone penetration tests

The damage caused by soil liquefaction during earthquakes is substantially affected by the total volume and spatial distribution of the liquefied soils. Therefore, a proper characterization of the spatial distribution of liquefaction potential of subsurface soils plays a critical role in earthquake hazard assessment and mitigation. In engineering practice, in situ tests, such as the cone penetration test (CPT), are widely used for evaluating soil liquefaction potential through, e.g., factor of safety (FS) against liquefaction, and the results of liquefaction assessment in a cross-section are needed for subsequent earthquake hazard analysis and mitigation. Traditional simplified methods only provide the liquefaction potential of soils at individual locations, and the results of liquefaction triggering evaluation at individual locations are often used to represent the whole site. This might lead to an unconservative estimation of earthquake-induced deformations and damage at a site where spatial variation of soil properties is significant. In addition, although CPT provides almost continuous sounding along vertical direction, measurements along horizontal directions are usually limited. This leads to the difficulty in proper application of geostatistical methods in liquefaction potential analysis. To address these challenges, a non-parametric and data-driven method is developed in this study to interpret a cross-section of FS with high spatial resolution directly from sparse CPT soundings. Based on the interpreted cross-section of FS, the predicted total volume and spatial distribution of liquefied soils are estimated. Both simulated and real CPT data are used to demonstrate and validate the proposed method, and the results show that the proposed method performs reasonably well.

Rigorous test of the performance of shear-wave velocity correlations derived from CPT soundings: A case study for Groningen, the Netherlands

An improved correlation between shear-wave velocity VS and cone penetration test (CPT) parameters was derived for the Groningen region, the Netherlands, using a set of 154 pairs of CPTs and seismic CPTs. An analysis of a high-quality subset showed that the subdivision into Holocene versus Pleistocene intervals or into cohesive versus non-cohesive intervals did not significantly improve correlation, nor did filtering out of the transition intervals between clay and sand in the non-cohesive dataset. In addition, using qt or qc resulted in similar regression results. We therefore propose a single new equation and we applied this to all available CPTs in the region. The analysis of the fitting bias showed that there is a trend relative to the measured VS. We therefore recommend performing SCPT measurements when very soft soils (VS < ~100 m/s) are encountered, rather than relying on the empirical correlation based on CPT parameters.The comparison of the VS30 values from CPTs to the model VS30 of Groningen shows that correlation is excellent: 90% of the calculated VS30 values from the CPTs fall within one standard deviation of the VS30 of the corresponding geological zone of the model. Although the resulting CPT-VS correlation is specific for the Groningen region, the approach can be applied to other regions with a paired SCPT-CPT dataset.

Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using Markov Chain Monte Carlo simulation

Cone penetration test (CPT) is one of the most commonly used in-situ methods for characterizing spatial variability of soil properties in geotechnical site characterization. It provides almost continuous soil responses when a cone is pushed into the ground at a constant rate. With the almost continuous CPT data, many geotechnical and geological engineering problems, such as evaluation of liquefaction potential and subsurface soil stratification, may be properly addressed. Although CPT provides almost continuous soil responses along the depth direction, the number of CPT soundings along a horizontal direction is usually small for a specific site, due to time, resources, or technical constraints. In these cases, interpolation or stochastic simulations are often needed to estimate CPT data at un-sampled locations. Several methods have been developed to address this issue, such as random field approach, geo-statistical methods, among others. However, determination of the parameters needed in random field theory and geo-statistical methods often requires a relatively large number of CPT soundings along horizontal directions, which is often not available in practice. Moreover, subsurface soils often contain multiple soil layers with spatially varying and unknown layer boundaries, and therefore CPT data are often non-stationary, leading to the difficulty in dealing with non-stationary CPT data due to different soil layers. This paper proposes a new and fast method that combines Bayesian compressive sensing with Markov Chain Monte Carlo (MCMC) simulation. The proposed method is data-driven, non-parametric, and directly applicable to non-stationary CPT data without pre-stratification of subsurface soils. In addition, to improve computational efficiency of MCMC simulation, a sequential updating technique is developed using Kronecker product. Both numerical and real-life examples are used to illustrate the proposed method. The results show that the proposed method is robust and performs well.

Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties

Spatial interpolation has been frequently encountered in earth sciences and engineering. A reasonable appraisal of subsurface heterogeneity plays a significant role in planning, risk assessment and decision making for geotechnical practice. Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements. However, successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures, which poses a great challenge for unexperienced engineers, particularly when only limited measurements are available. Data-driven machine learning methods, such as radial basis function network (RBFN), require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data, particularly when measurements are sparse. Conventional RBFN, however, is direction independent (i.e. isotropic) and cannot quantify prediction uncertainty in spatial interpolation. In this study, an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated, but also quantifies uncertainty in spatial interpolation. The proposed method is illustrated using numerical examples of cone penetration test (CPT) data, which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction. In addition, a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches, namely, Multiple Point Statistics (MPS) and Bayesian Compressive Sensing (BCS). The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input. Moreover, the prediction accuracy of all the three methods improves as the number of measurements increases, and vice versa. It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.

Probabilistic assignment of effective friction angles of sands and silty sands FROM CPT using quantile regression

ABSTRACTThis paper develops new probabilistic correlations for assigning design values of effective friction angle () of clean to silty sands from cone penetration test (CPT) data. New transformation models are proposed using a high-quality database of undisturbed sands. The sand samples have been collected using special methods (freezing, gel samplers, Mazier tube) and tested in the laboratory triaxial compression mode. The CPT soundings have been performed using electronic piezocone penetrometers (, , ) advanced at the standard rate of 20 mm/s. Two CPT normalisation schemes (, ) are investigated. Correlations are obtained through quantile regression, a statistical method which allows the assignment of regression functions which are directly and quantitatively associated with probabilities of exceedance. The derived relationships allow the geoengineer to estimate the effective stress friction angle at any desired level of conservatism. The paper concludes by demonstrating how to apply the results practically in geotechnical design.

Cone penetration test (CPT)-based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling

A novel method is developed in this study for soil classification and zonation in a two-dimensional (2D) vertical cross section using cone penetration tests (CPTs). A CPT is usually performed vertically and the number of CPT soundings in a site is often limited in geotechnical engineering practice. It is, therefore, difficult to properly interpret CPT results along the horizontal direction or accurately estimate the horizontal correlation length of CPT data. The method proposed in this study bypasses the difficulty in estimating horizontal correlation length and provides proper identification of subsurface soil stratification (i.e., soil layer number is constant along horizontal direction) and zonation (i.e., soil layer number varies along horizontal direction) in a 2D vertical cross section directly from a limited number of CPT soundings. The proposed method consists of three key elements: 2D interpolation of CPT data using 2D Bayesian compressive sampling; determination of soil behavior type (SBT) using a SBT chart at every location in the 2D section, including locations with measurements and unsampled locations; and soil layer or zone delineation using an edge detection method. Both simulated and real data examples are used to illustrate the proposed method. Results show that the method performs well even when only five sets of CPT soundings are available.

A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data

This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.

A method for predicting the influence of matric suction changes on CPT tip resistance

The Cone Penetration Test (CPT) is a valuable tool for continuous soil profiling and estimating soil properties. A CPT sounding will often pass through the active zone of an unsaturated soil profile, near the ground surface where moisture conditions and matric suction can vary considerably with depth and time. For many projects, the active zone is also significant to the support of shallow foundations and overlying embankments. However, the strength and stiffness of the unsaturated soils in this zone can be significantly affected by variations in the moisture content and matric suction. Likewise, the cone penetration tip resistance and sleeve friction will be influenced by the variations in moisture conditions and suction. Therefore, it is important to consider the soil moisture conditions when CPTs are conducted relative to the moisture conditions assumed for geotechnical design. This paper presents a method for predicting changes in tip resistance as result of changes in matric suction. The method extends the bearing capacity theory to predict changes in cone tip resistance due to changes in matric suction. CPT data collected at test sites, during wet and dry seasons and representing different soil types, are presented to demonstrate the viability of the proposed method.

Spatial Correlation of Cone Tip Resistance for Soil in Nasiriyah

Background:Quantification of soil property spatial variations is an important step in any reliability-based design. Little stochastic parameter information about the soil in Nasiriyah, which is in southern Iraq, is available.Methods:In this paper, the Scale Of Fluctuation (SOF) for the site soil is examined, which is suggested for construction of the refinery fuel station project as no random parametric quantity has been studied in this region. A Cone Penetration Test (CPT) was performed as part of the site investigation to a depth of 20 m in the vertical direction, and 24 CPTs were analyzed within the site. The spatial correlation was computed using four methods, including Single Exponent (SNE), Square Exponent (SQE), Cosine Exponent (CSE) and Second-Order Markov (SOM). Identification of the soil type depended on the most recent classification chart, which is based on CPT results. The spatial correlation was evaluated for the vertical direction considering the cone tip resistance (qc) component. Three trend functions were applied to all CPT soundings, including linear, quadratic and cubic polynomials, which were utilized to transform the non-stationary data to stationary data. Three modes of soil were employed, including the eight-meter soil layer, a twelve-meter soil layer and the entire twenty meters of soil, which includes both layers.Results and Discussion:The mean values of SOF were 0.54 m, 0.53 m, and 1.73 m for soil layers 8 m, 12 m, and 20 m, respectively. The high value of the last mean is attributed to the 20 m of stratification in the ground. This study also indicates that the SOF decreases as the polynomial degree increases, which is due to enhanced fitting. The coefficient of variation (COV) for the SOF shows little variability for most of the studied soil cases.

A semi-supervised clustering-based approach for stratification identification using borehole and cone penetration test data

Borehole drilling and cone penetration test (CPT) are frequently employed site investigation methods for identifying subsurface stratification. However, these two methods have their respective pros and cons, and their corresponding soil type classification protocols are different. Therefore, an approach that can jointly interpret raw data from both investigation methods and provide unified soil classification results is in great demand. Motivated by the aforementioned point, this paper presents a novel semi-supervised clustering based stratification identification approach using information from both boreholes and CPT logs. The proposed approach is established on a hidden Markov random field (HMRF) framework so that the supervision constraints could be introduced by using borehole data during the clustering of CPT sounding samples. Further, the presented approach employs a Monte Carlo Expectation Maximization (MCEM) algorithm to perform the clustering process, which enables estimating the subsurface stratification in a probabilistic manner. The performances of the proposed approach are evaluated using real-world site investigation data. The test results indicate that the proposed approach is effective and robust for identifying subsurface stratification.

Modification of the liquefaction potential index to consider the topography in Christchurch, New Zealand

In recent years, many have mapped the liquefaction potential index (LPI) to describe the liquefaction hazard at regional scale. Several investigators have calibrated the LPI to field observations of liquefaction-induced ground failure after a number of major earthquakes; the significance of LPI values and their correlation with the severity of liquefaction-induced ground failure can vary greatly depending on the calibration. In this study, the LPI was computed at more than 1200 cone penetration test soundings across the Christchurch area, New Zealand, using peak ground accelerations from the 2011 Christchurch Earthquake. Based on detailed field observations of liquefaction-induced ground failure after the earthquake, it was shown that the LPI has potential for discriminating between areas with no liquefaction-induced ground failure hazard and areas that may experience liquefaction-induced ground oscillations and settlement; however, the LPI performed poorly at sites where severe ground failures occurred, especially at sites that experienced lateral spreading. As many researchers have found a positive correlation between lateral spreading and the proximity and depth of a nearby free-face (i.e., a steep topographic depression, river channel, etc.), a new LPI framework was proposed that includes a parameter named the free-face ratio (FFR). FFR was shown to have a significant correlation with field observations of lateral spreading in Christchurch. It was shown that by incorporating FFR into the LPI framework, modified LPI values are positively correlated with the severity of field observations of liquefaction-induced ground failure in Christchurch. It was also shown that maps can be produced based on LPI and FFR, and such maps rarely underpredict the liquefaction-induced ground failure hazard, unlike maps based on the unmodified LPI.