Cone Penetration Test Profiles Research Articles

Quantifying CPT cone factors in clays derived from weathered mudstone

The empirical interpretation of cone penetration test (CPT) cone factors ( N k ) can be subject to considerable variability for clays derived from weathered mudstones, leading to significant deviations in the estimation of undrained shear strength ( S u ). This paper presents a comparison of triaxial and CPT data from a site investigation in clays derived from weathered mudstones in central England. Corrected cone factors ( N kt,UU ) were derived from a one-to-one comparison of 94 pairs of unconsolidated, undrained triaxial and CPT data from equivalent depths. The performance of the cone factors was evaluated using a training set (75 pairs) and a test set (19 pairs). A parametric study was used to explore the variability of N kt,UU , quantified using the coefficient of variation (COV Nkt,UU ), for varied separation distance thresholds ( D s ) between individual triaxial and CPT data. The absolute deviation between the laboratory shear strength ( S u(Lab) ) and that predicted from CPT profiles (S u(CPT) ) was not sensitive to N kt,UU values in the range 25 < N kt,UU < 31. The parametric study showed that D s could be increased from 50 to 250 m, to include more data pairs for estimates of N kt,UU , without substantially increasing COV Nkt,UU .

Evaluating statistical homogeneity of cone penetration test (CPT) data profile using auto-correlation function

Cone penetration test (CPT) data profile is often numerically modelled using random field theory and based implicitly on a fundamental assumption: statistical homogeneity of the preprocessed stationary CPT data profile. In classical random field theory, statistical homogeneity evaluation may only be achieved if the CPT data profile is infinite in length. While in engineering practice, it is often the case that a CPT data profile has limited sounding depth. This leads to a critical question that whether the CPT data profile satisfies statistical homogeneity assumption to enable proper random field modelling. This paper proposes a novel non-parametric method for evaluating statistical homogeneity of a single CPT data profile. A novel unified representation of CPT data profile pattern is developed in this study using discrete cosine transform (DCT)-based auto-correlation function (ACF). Statistical homogeneity evaluation of a CPT data profile is reformulated as statistical analysis of pattern similarity between the original CPT data profile and its partial segment profiles. The proposed method is general since it is distribution-free and does not require a parametric correlation function. The proposed method performs well on both simulated and real examples and can identify the possible locations of heterogeneity in a CPT profile.

Development of the heat flow cone penetration test (HF-CPT)

The heat flow cone penetration test (HF-CPT) provides in-situ values of thermal conductivity (patent pending). The HF-CPT test records include (1) heat flow (HF) measurements acquired by a CPT add-on module, i.e. heating power and temperature versus time and (2) cone penetration test (CPT) measurements, i.e. cone resistance, sleeve friction and pore pressure versus depth and time. The test method requires a short interruption of the continuous CPT penetration phase, to allow stationary HF heating and cooling cycles.
 Values of thermal conductivity are derived similarly to the principles for laboratory thermal needle probes described in common ASTM standards, particularly [1].
 Data processing makes use of an advanced interpretation method that accounts for the short-cylinder effects of the HF module and short timeframe heat fluxes. The novel interpretation method includes inversion of a numerical forward model of the interaction between the heat flow module and the surrounding soil. The interpretation method also integrates standard CPT results, such that both a semi-continuous thermal conductivity profile and a continuous standard CPT profile are obtained.
 Validation of the interpretation method included comparison of thermal conductivity values derived from other test methods, notably laboratory transient plane source tests [1], in-situ thermal needle probe tests (based on [2]) and thermal cone penetration tests [3]. Figure 1 presents an example of validation results for predominantly clay soil. The results are seen to closely match those derived from an in-situ needle probe. As the HF-CPT measurements were taken two metres apart horizontally from the in-situ needle probe measurements, some deviation between the results is to be expected due to heterogeneity of the soil.

Generation and evaluation of synthetic cone penetration test (CPT) data using various spatial interpolation techniques

The cone penetration test (CPT) has been widely used in many geotechnical engineering applications, including soil identification and classification, and evaluation of different soil properties. However, the uncertainties associated with site variation are typical characteristics of subsurface soil conditions that cannot be ignored. Therefore, the effect of site variability on the correlated soil properties from collected field data, such as CPT data, obtained from discrete locations across the site needs to be evaluated. In this study, six well-established spatial interpolation techniques — ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) — were investigated to evaluate the best interpolation method for incorporating site variability. Six CPT test sites were used to evaluate the performance of these spatial interpretation methods. For each site, CPT profiles at specified locations were generated using the different interpolation techniques, and the generated CPT profiles were compared with the measured CPT profiles. The best-fit line of measured versus predicted cone tip resistance (qc) values, mean bias factor (λ), coefficient of variation (COV), and root mean square error (RMSE) were calculated for each CPT profile and used as a criteria for evaluating the different spatial interpolation methods. The results of this study demonstrated the ability of these spatial interpolation methods for generating CPT profiles with good accuracy. The slope of best-fit line of measured versus predicted qc ranges from 0.93 to 0.95, the mean of λ ranges from 0.90 to 0.98, and the COV ranges from 0.34 to 0.53. The IDW, OK, and SK showed the best spatial interpolation methods (in order) for four out of the six CPT sites. For site 4, OK, IDW, and SK showed the best spatial interpolation methods (in order); while for site 5, the three best spatial interpolation techniques are SK, IDW, and OK (in order).

Predicting blast-induced liquefaction within the New Madrid Seismic Zone

The Turrell Arkansas Testing Site (TATS) is located in Northeastern Arkansas, United States. The site consists of approximately six meters of overconsolidated clay underlain by loose liquefiable sand deposits. Numerous traditional (rotary-wash boring with sample collection) and advanced (seismic piezocone soundings) geotechnical investigations have been performed at this site. A controlled-blasting testing program was also performed at the TATS to determine the blasting layout (the appropriate amount of explosive charges, the detonation delays, and the charge spacing), and to verify induced liquefaction of the soil deposit at a testing site located within the New Madrid Seismic Zone (NMSZ). The results obtained from the installed transducers and the pre- and postblast cone penetration tests (CPT) are discussed. Although, the CPT were performed when the excess porewater pressures were dissipated, a review of CPT profiles after blasting showed no evidence of increase of cone tip resistance and sleeve friction. Due to the small amount of explosive charge weight that was used, the excess porewater pressure ratio values only increased above the unity at the depth of 11.30 m. A review of the existing empirical models used to predict blast-induced porewater pressure responses and liquefaction is presented. A new empirical model that accounts for the in-situ soil properties, to estimate the excess pore pressure ratio, was developed and is presented herein.

Two-Dimensional Random Field Conditioned by CPT Data: Case Study

The presented geotechnical random field (GRF) algorithm is utilized to realize the soil inherent variability based on in situ site investigation results. This paper aims at investigating the effect of cone penetration test (CPT) borehole arrangements in horizontal plane on slope stability reliability analysis considering stochastic fields of soil design parameters via the conventional stationary along with the non-stationary random field adopted in the GRF approach. An advantage of the GRF algorithm is that it yields conservative results in comparison to the conventional stationary random field theory; therefore, using non-stationary random field underestimates slope stability reliability. The current study tried to highlight the importance of the availability of in situ profiles of soil strength parameters when stability analysis of geotechnical structures is concerned. Slope stability was chosen to interpret the role of the CPT profiles in the adjacent site in determining the factor of safety of the slope against shear failure. Furthermore, the results of the case under study showed that when the geometric dimensions of the slope are too small compared to the horizontal correlation length, more CPT borehole data availability leads only to very slightly conservative results in comparison to the case with single available CPT profile.

Statistics of model factors in reliability-based design of axially loaded driven piles in sand

This paper compiles 162 reliable field load tests for axially loaded driven piles in sand from previous studies. The L1–L2 method is adopted to interpret the measured resistance from the load–settlement data. The accuracy of resistance calculations with the ICP-05 and UWA-05 methods based on cone penetration test profile is evaluated by the ratio (bias or model factor) of the measured resistance to the calculated resistance. A hyperbolic model with two parameters, where the load component is normalized by the measured resistance, is utilized to fit the measured load–settlement curves. The means, coefficients of variation, and probability distributions for the resistance model factor and the hyperbolic parameters are established from the database. Copula theory is employed to characterize the correlation structure within the hyperbolic parameters. The statistical properties of the model factors are applied to calibrate the resistance factors in simplified reliability-based designs of closed-end piles driven into sand at the ultimate and serviceability limit state by Monte-Carlo simulations. A simple example is provided to illustrate the application of the proposed resistance factors to estimate the allowable load for an allowable settlement at the desired serviceability limit probability.

Cone penetration data classification by Bayesian inversion with a Hidden Markov model

This study examines the application of the Hidden Markov model (HMM) to the soil classification based on Cone Penetration Test (CPT) measurements. The HMM is formulated in the Bayesian framework and composed of a Markov chain prior and a Gaussian likelihood model. The application of the Bayesian framework is considered as suitable because it allows for the integration of different sources of information commonly available in a CPT-based soil classification. The occurrence of different soil classes along a CPT profile is modeled with the Markov chain, while the Gaussian likelihood model establishes a relation between the different soil classes and CPT measurements. Preliminary performance of the HMM is examined on the classification of CPT measurements from the Sheringham Shoal Offshore Wind Farm.

Geophysical and Geotechnical Studies of a Proposed Structure at Akure, Southwestern Nigeria

Geophysical and geotechnical studies were carried out at a proposed location for the construction of a multi-storey structure in Akure, Southwestern, Nigeria. The aim was to ascertain the suitability of this location for both Founding and Engineering structures. The geophysical investigation involved the Vertical Electrical Sounding (VES) technique using the Schlumberger configuration, Dipole-Dipole Horizontal Profiling and a geotechnical investigation. A total of twenty-one (21) VES and five (5) Cone Penetration Test (CPT) locations were occupied within the study site. Dipole-Dipole Horizontal Profiling was occupied along traverses 1 and 2 within the investigated area. The electrode separation varies from 1 to 75 m. The investigation delineated three major layers which are topsoil, which is excavated before any foundation is laid. The second layer delineated was lateritic and the last was weathered layer. From the result obtained, depth to lateritic layer ranges from 1.1 to 9.0 m while resistivity defining the lateritic layer ranges from 150 Ωm to 792 Ωm. Some of the sounding curves generated over the VES stations and Dipole-Dipole Horizontal Profiling fairly correlated with those of the CPT profile. The high cone penetration resistance recorded at CPT point 4 and 5 is manifested as high geoelectric resistivity values recorded at VES 13. This shows that the soil has fairly low clay content. It also seen from the study that the geophysical studies has a greater depth penetration, and it also provide better layer characterization compared to geotechnical studies. The choice of foundation material, clay content and topography elevation should be taken into cognizance, since the load bearing capacity of the lateritic layer was appreciably high.

Effect of excavation unloading on p-y curves for laterally loaded piles

Field test and numerical analysis were conducted to investigate the soil stress variation and lateral pile response to whole excavations from cone penetration tests (CPTs). The effects of excavation unloading on the performance of CPT cone resistances and p-y curves were examined. The p-y responses were related to the excavation-induced both stratigraphic variation and soil stress relief. Based on CPT profiles, the numerical calculations of the lateral pile responses before and after excavations were obtained and compared using a CPT-based p-y method. Finally, the suggestion on the design of laterally loaded piles considering excavation unloading was given.

The role of copulas in random fields: Characterization and application

The spatial fluctuation of material properties is commonly modeled using random fields. However, descriptions of random fields in terms of marginal distributions and correlation structure are actually incomplete. The copulas that specify the dependence structure underlying the joint probability distribution over random fields are not provided and a Gaussian dependence structure is assumed without proper validation in practice. This study, therefore, tests the fitness of several non-Gaussian dependence structures in addition to the common use of the Gaussian dependence structure. A soil profile of cone penetration test (CPT) data is collected, and a pair-wise likelihood estimate approach is adopted to identify the best fit dependence structure. The results reveal that both qc and fs of the CPT profile exhibit the same non-Gaussian dependence characteristics, which challenges the Gaussian assumption. Generation of random fields with a non-Gaussian dependence structure is then proposed. Two slope examples where the spatial fluctuation of shear strength is respectively modeled using stationary and non-stationary random fields are used to investigate the deviation in failure probability due to different dependence structures. The effect of the dependence structure is non-trivial except when the random field is highly ragged or highly smooth. The results highlight the importance of copulas for dependence characterization in random fields.

Reliability-based assessment of drilled displacement piles bearing capacity using CPT records

ABSTRACTDrilled displacement piles (DDPs) are known as an alternative to conventional foundations in coastal areas, given the elimination of environmental impacts and difficulties caused by installation process of driven piles and more consistency with environment. Despite increasing employment of these piles, the extent of research works does not yet suffice the requisites to reach a routine design. This paper aims to analyze six cone penetration test (CPT)-based methods of determining the bearing capacity of DDP. The statistical and reliability-based approaches were used in two parts of assessing performance of the methods with respect to soil–pile characteristics followed by evaluating reliability of the prediction outcome. A database is compiled including 65 DDP load tests with adjacent CPT profiles. Performance of the methods are analyzed. Finally, a reliability parameter, i.e., confidence interval, is introduced to demonstrate a more realistic insight into the evaluations by expressing performance of the methods in terms of a range for possible average values of the predictions ratios, rather than simply an arithmetic mean. The study reveals that the commonly used CPT-based methods which have not been specifically developed for DDP show great potential for design. The results indicate that the investigated methods can have promising performance if some modifications are applied.

Bayesian approach for probabilistic site characterization assimilating borehole experiments and Cone Penetration Tests

This study investigates a Bayesian approach for assimilating data from borehole experiments and Cone Penetration Tests (CPTs) for site characterization (e.g., soil compression modulus). The spatial variability of compression modulus is depicted by random field theory. A Bayesian inverse modeling method is established by combining prior information, in the form of empirical knowledge, with observations, in the form of borehole experiments and in-situ CPT profiles to calculate posterior estimates of compression modulus at unsampled locations. The approach classifies all relevant data as either direct or indirect, and uses geostatistical tools in a Bayesian algorithm to estimate the posteriors. The local uncertainty of indirect data is analyzed using maximum entropy theory and the Bayesian algorithm simulates the posterior prediction of unsampled locations given direct data, plus integrating out the local uncertainties on joint Probability Density Function (PDF) of indirect data. To validate its effectiveness, Bayesian inverse modeling method is applied to a shallow load bearing soil layer located at the National Convention and Exhibition Center in Shanghai. Spatial estimates are refined in comparison to those from Kriging method. It is concluded that the above approach appears promising as a framework for site characterization given multiple types of observation.

Generating non-stationary random fields of auto-correlated, normally distributed CPT profile by matrix decomposition method

This paper emphasises the true realisation of Cone Penetration Test (CPT) profiles considering non-stationary nature of the data. Formulation of stationary random field theory has been modified and adapted to non-stationary state in order to take into account the mean and variance variability for soil properties. Multi-variance correlation matrix along with the Cholesky decomposition technique was employed to produce realisations of non-homogenous and non-stationary random fields of CPT profiles. A piecewise and segmental data realisation according to the lithology and site class specifications acquired directly from CPT data is adopted in this study so as to render an accurate data simulation. For validation of proposed method 8 CPT test profiles collected from Urmia Lake site have been introduced and simulated by the stationary and non-stationary algorithms. The mean correlation coefficient between the actual CPT data profiles and related realisations along with some other important statistical parameters and their coefficients of variation strongly demonstrate that non-stationary random field generation technique gives quite better accuracy, by comparison to the conventional stationary random field generation scheme.

Influence of particle size on the correlation between shear wave velocity and cone tip resistance

The construction of the Péribonka dam involved deep compaction of its foundation using vibroflotation and dynamic compaction. Surface wave testing was used, in addition to classical tests (cone penetration tests (CPTs) and standard penetration tests (SPTs)) for the assessment of vibrocompaction. More than 900 shear wave velocity (Vs) and 1000 CPT profiles were obtained. This set of tests performed prior to and following vibrocompaction constitutes an important data bank, used in this study to establish a relationship between normalized shear wave velocity, Vs1, normalized tip resistance, qc1, and mean grain size, D50. Using the Péribonka project data obtained on fairly coarse sands in conjunction with the Canadian Liquefaction Experiment (CANLEX) project data obtained on fine sands has confirmed the significant effect of particle-size distribution on the relationship between Vsand qc. The paper proposes a correlation between Vs1, qc1, and D50for uncemented and Holocene-age granular soils in continuity with the relation developed by Wride et al. from the CANLEX project.

Estimating spatial correlation structures based on CPT data

Spatial correlation structures are usually used while modelling inherent uncertainty associated with soil properties. To retrieve this information a sufficient number of measurements and equal intervals between measurements or samples are always required. However, in practice, it is not usual to conduct site investigation in that way subject to a limited budget. Therefore, it is of interest to understand the possible bias created by using samples with unequal intervals. This research focuses on estimating the vertical spatial correlation structures of cone penetration tests (CPT) data conducted at a liquefaction site in Taiwan. The appropriate fitting range used in calibrating the correlation model is also discussed. The results show that cone tip resistance has larger correlation distances than that of sleeve friction. The estimated correlation distances of the CPT profiles are similar while using an equal sampling interval within a certain fitting range (denoted as the critical interval of measurements in this research). The results indicate that the effect of using different curve-fitting ranges is relatively insignificant for correlation distances of sleeve friction, whereas the correlation distances of cone tip resistance are more sensitive to different curve-fitting ranges. Moreover, the mean values of correlation distances are more constant for the equal sampling interval cases than for the unequal interval cases.

Mapping liquefaction potential of aged soil deposits in Mount Pleasant, South Carolina

Liquefaction potential of aged soil deposits in Mount Pleasant, South Carolina, based on the 1886 Charleston earthquake is characterized in this paper. The characterization involves reviewing available first-hand accounts of 1886 ground behavior, analyzing cone penetration test (CPT) and shear wave velocity data, and correlating the results with geology. Careful review of the first-hand accounts reveals that nearly all cases of surface effects of liquefaction can be associated with the younger sand deposits that lie adjacent to the harbor, rivers, and creeks. Only one documented case of minimal surface effect of liquefaction can be definitely associated with the older sand deposits of the 100,000-year-old Wando Formation. Ratios of measured-to-estimated shear wave velocity indicate that the younger sand deposits and the older sand deposits have measured velocities that are 9% and 38%, respectively, greater than 6-year-old sand deposits with the same CPT tip resistance. Liquefaction potential is expressed in terms of the liquefaction potential index (LPI) proposed by Iwasaki and others. LPI values for the Wando sands computed from the CPT profiles are incorrectly high, if no age corrections are applied. If age corrections are applied, computed LPI values match well the observed field behavior in both the younger sands and the older sands. The results are combined with a 1:24,000 scale geologic map to produce a liquefaction potential map of Mount Pleasant. The findings of this study agree remarkably well with a previous liquefaction potential study of aged soil deposits on Charleston peninsula.

Database Assessment of CPT-Based Design Methods for Axial Capacity of Driven Piles in Siliceous Sands

Numerous cone penetration test (CPT)-based methods exist for calculation of the axial pile capacity in sands, but no clear guidance is presently available to assist designers in the selection of the most appropriate method. To assist in this regard, this paper examines the predictive performance of a range of pile design methods against a newly compiled database of static load tests on driven piles in siliceous sands with adjacent CPT profiles. Seven driven pile design methods are considered, including the conventional American Petroleum Institute (API) approach, simplified CPT alpha methods, and four new CPT-based methods, which are now presented in the commentary of the 22nd edition of the API recommendations. Mean and standard deviation database statistics for the design methods are presented for the entire 77 pile database, as well as for smaller subset databases separated by pile material (steel and concrete), end condition (open versus closed), and direction of loading (tension versus compression). Certain methods are seen to exhibit bias toward length, relative density, cone tip resistance, and pile end condition. Other methods do not exhibit any apparent bias (even though their formulations differ significantly) due to the limited size of the database subsets and the large number of factors known to influence pile capacity in sand. The database statistics for the best performing methods are substantially better than those for the API approach and the simplified alpha methods. Improved predictive reliability will emerge with an extension of the database and the inclusion of additional important controlling factors affecting capacity.