Cone Penetration Test Data Research Articles

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.

Cone penetration testing in unsaturated soils

Abstract Results of Cone Penetration Tests (CPTs) on compacted clayey silt under different moisture contents and matric suctions conducted in a calibration chamber are presented along with results of CPTs conducted in unsaturated clayey soil at two test sites over a period of approximately two years. CPT data obtained from published literature are presented for comparison. The accumulated data demonstrate the dependence of penetration resistance on moisture content and suction and reveal empirical relationships that can be exploited for preliminary assessment of tip resistance under changing moisture conditions. A preliminary treatment of tip resistance in unsaturated soil using bearing capacity theory is presented and shows promise as a semi-empirical method for assessing changes in tip resistance as a function of matric suction.

Drained cavity expansion analysis with a unified state parameter model for clay and sand

This paper presents an analytical solution for drained expansion in both spherical and cylindrical cavities with a unified state parameter model for clay and sand (CASM). The solution developed here provides the stress and strain fields during the expansion of a cavity from an initial to an arbitrary final radius. Small strains are assumed for the elastic region and large strains are applied to soil in the plastic region by using logarithmic strain definitions. Since its development, the unified CASM model has been demonstrated by many researchers to be able to capture the overall soil behaviour for both clay and sand under both drained and undrained loading conditions. In this study, the CASM model is used to model soil behaviour whilst a drained cavity expansion solution is developed with the aid of an auxiliary variable. This is an extension of the undrained solution presented by the authors in 2017. The parametric study investigates the effects of various model constants including the stress-state coefficient and the spacing ratio on soil stress paths and cavity expansion curves. Both London clay and Ticino sand are modelled under various initial stress conditions and initial state parameters. The newly developed analytical solution highlights the potential applications in geotechnical practice (e.g., for the interpretation of cone penetration test data) and also provides useful benchmarks for numerical simulations of cavity expansion problems in critical state soils.

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.

Empirical Correlation for Estimating Shear-Wave Velocity from Cone Penetration Test Data for Banks Peninsula Loess Soils in Canterbury, New Zealand

AbstractSeismic piezocone data obtained from Banks Peninsula loess soil sites in the Port Hills south of Christchurch, New Zealand, are used with multiple linear regression to develop a new Banks P...

Estimating soil resistance at unsampled locations based on limited CPT data

An assessment of soil properties at unsampled locations is a common requirement during the design of a geotechnical structure/system. Such an assessment is challenging due to inherent soil variability and the limited number of in situ tests currently available. Here we present a three-dimensional conditioned random field approach for the estimation of anisotropic soil resistance at unsampled locations based only on data from a limited number of cone penetration tests (CPT). The novelty of this work is that the measured CPT data at arbitrary locations can be combined with random field theory to update the estimated soil properties in three dimensions. The accuracy of the estimation can be improved significantly using the proposed method. A case study in Australia is conducted to illustrate the procedure and to assess the capability of the method. The results indicate that the mean values of the conditioned random fields are good estimates of the soil resistance at unsampled locations. The prediction error of the normalized cone penetration resistance is about 0.08 when there are only six CPT tests. A unique feature of this method is its ability to obtain high-resolution results of soil properties in three-dimensional space using a limited number of CPT tests.

Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation

Random field theory has been increasingly used in probabilistic geotechnical analyses over the past few decades, where a random field generator with random field parameters is needed to simulate random field samples (RFSs) of interest. Estimation of random field parameters, particularly correlation functions or correlation length, generally requires extensive measurements. However, the data gathered from site characterizations are usually sparse, particularly for small or medium sized projects. Therefore, it is difficult to provide an accurate estimation on random field parameters, and the random field parameters estimated and subsequently used in RFS generation might contain significant uncertainty. This leads to a challenge of properly simulating RFSs in consideration of such uncertainty. This paper aims to address this challenge by developing a novel random field generator, which is capable of directly generating RFSs from sparse measurements obtained during site characterization and properly accounting for uncertainty associated with interpretation of sparse data. The proposed generator is based on Bayesian compressive sampling (BCS) and Karhunen–Loève (KL) expansion, and it is denoted as BCS–KL generator. The proposed BCS–KL generator is illustrated and validated through both simulated data and 30 sets of cone penetration test data measured throughout the world.

3D FEM approach for laterally loaded monopile design

Over the past 5 years, a substantial research effort aimed at optimising the design of offshore wind turbines has led to significant reductions in the projected cost of developing offshore wind. Optimising the geotechnical design of these structures, through modern analysis techniques such as 3D Finite Element Modelling (FEM), has played a key role in helping to reduce costs. This paper presents a methodology for accurately modelling monopile behaviour using Cone Penetration Test (CPT) data to calibrate the non-linear stress dependent Hardening Soil (HS) model. The methodology is validated by comparing the modelled behaviour to field tests on a range of pile geometries. The paper also demonstrates how the soil-pile reaction response curves can be extracted from the FE model by isolating the stresses on each element of the pile. The contribution of each component to the overall lateral resistance is shown to vary with the pile geometry and is examined using the extracted soil reaction curves.

Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks

Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the commencement of construction or the design of foundation systems. This paper presents a means of automating classification for fine-grained soils, using a feed-forward ANN (Artificial Neural Networks) and CPT (Cone Penetration Test) measurements. Thus representing a significant saving of both time and money streamlining the construction process. 216 pairs of laboratory results and CPT tests were gathered from five locations across Northern Croatia and were used to train, test, and validate the ANN models. The resultant Neural Networks were saved and were subjected to a further external verification using CPT data from the Veliki vrh landslide. A test site, which the model had not previously been exposed to. The neural network approach proved extremely adept at predicting both ESCS (European Soil Classification System) and USCS (Unified Soil Classification System) soil classifications, correctly classifying almost 90% of soils. While the soils that were incorrectly classified were only partially misclassified. The model was compared to a previously published model, which was compiled using accepted industry standard soil parameter correlations and was shown to be a substantial improvement, in terms of correlation coefficient, absolute average error, and the accuracy of soil classification according to both USCS and ESCS guidelines. The study confirms the functional link between CPT results, the percentage of fine particles FC, the liquid limit wL and the plasticity index IP. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive.

Non-stationary realisation of CPT data: considering lithological and inherent heterogeneity

ABSTRACTThe main sources of uncertainty in the soil specification and mechanical behaviour consist of the lithological and heterogeneous randomness of soil deposits. It is quite obvious that the cone penetration testing (CPT) data and the variation of soil characteristics are not stationary. Hence, this paper investigates a new approach to realise a CPT data, taking both sources of uncertainty into consideration. In this regard, the first part of this study illustrates a simple approach to stratify the CPT data, using the Eslami–Fellenius chart of classification. In the second part, the non-stationary algorithm of generating random field is introduced to generate a multi-layer random field. This algorithm takes account of each layer’s statistical properties (i.e. standard deviation, mean, and the trend value), separately. To validate the proposed approach, 41 case histories from different worldwide sites, have been regenerated by considering both the stationary and non-stationary algorithms. The correlation coefficient between real and realised CPT data has been employed to show that the proposed non-stationary algorithm can simulate the CPT data more accurately in comparison with the stationary algorithm.

Review of shear-wave velocity prediction equations based on piezocone penetration test data: example from Yangtze River floodplain deposits at Nanjing, Jiangsu Province, China

Small-strain parameters of soils play an important role in modern engineering geology. A research database of seismic piezocone test (SCPTu) results has been collected to develop empirical correlations for shear-wave velocity ( V s ) prediction of recent floodplain deposits at Nanjing in the lower reaches of the Yangtze River. First, several existing cone penetration test (CPT)– V s or CPTu– V s correlations for general soils have been investigated. Four indices were used in the assessment, including the ratio of the estimated to measured V s values, the root mean square error, the ranking index and the ranking distance. It is observed that all the selected correlations are biased towards overestimation or underestimation of V s values for such floodplain deposits, demonstrating the need for a site-specific V s prediction model. Then, new correlations for Yangtze floodplain deposits were developed through multiple linear regression, the performance of which has been assessed through comparisons of predicted and observed V s values. The recommended correlation is entirely based directly on CPT data ( q c , f s and z ). The verification and calibration of this newly developed correlation for floodplain deposits constitute a useful contribution to the practice of geologists and engineers not only in the Yangtze delta region, but also in other similar depositional environments worldwide.

Efficient random field modeling of soil deposits properties

The autocorrelation function (ACF) of the soil profile in some sites in Shandong province, China is studied using cone penetration test (CPT) data. This is done in the context of a random field modeling of the soil deposits. It is found that the different types of soil profile have different stochastic parameters, and there is no obvious trend along the depth of the soil profile. Thus, the soil profile is examined within each layer. Numerical values for three existing analytical (ACF) models are derived by the least squares fitting approach for the different types of soil. Further, comparisons of the autocorrelation function between the tip resistance and sleeve friction were examined. Based on the autocorrelation data analysis, a new autocorrelation model, named linear-exponential-cosine (LNCS), is considered with differentiability at the origin of the spatial lag axis, and alternating sign along this axis. For all of the four ACF models, a related integral equation is numerically solved for determining the associated Karhunen-Loeve (K-L) representation. In this regard, it is noted that the new model is not only more physics-consistent, but also yields quite good computational efficiency. In the end, the random field of the soil profile is modeled using a two-dimensional K-L expansion with the new model, assuming separability in two dimensions.

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (Pu) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of Pu based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is Pu for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of Pu with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.

Prediction of drained soil shear strength parameters of marine deposit from CPTu data using GMDH-type neural network

ABSTRACTDue to a few limitations through laboratory testing for determining soil shear strength parameters, cone penetration test (CPT) has been realized as a multipurpose apparatus with an acceptable performance, especially in soft to medium deposits. The known methods for obtaining shear strength parameters using CPT data, estimate conservative values for φ (internal friction angle) in granular and Su (undrained soil shear strength) in cohesive soils. As the determination of the shear strength parameters (shear strength (τ), Cohesion (C), and friction angle (φ))—especially at depths—is relatively costly and time-consuming, there is a need to develop models that can handle simply determinable properties. In the present study, the suitability of the group method of data handling (GMDH)-type neural network (NN) and genetic algorithms (GAs) to predict the C and φ of marine soils have been investigated. To derive GMDH models, a database containing 50 datasets compiled from geotechnical investigation sites in Iran, has been used. Results show that the polynomial models introduced in the current study are appropriate models to estimate shear strength parameters (C and φ) which represented accurate results. To achieve better correlation, using ln form of cone tip resistance (ln qc) have been suggested as input parameter. The performance of the GMDH models has been compared with other available correlations, and it has been demonstrated an improvement in estimating the marine soil shear strength parameters. Finally, a sensitivity analysis has been conducted on the proposed models, showing that the proposed φ is considerably influenced by changing the Rf value.

Cone penetration testing in thinly inter-layered soils

The effect of a soft layer on the cone resistance of a sand layer has been studied extensively, but the same cannot be said for the case of multi-layered deposits with many (thin) alternating soil layers. For geotechnical analyses based on cone penetration test data it is difficult to estimate a representative value of the cone resistance when encountering alternating thin layers of clay or peat and sand. This paper presents physical modelling tests to understand the ‘value’ of the cone resistance in thinly multi-layered soil deposits. The test results are compared with existing analytical methods. It is concluded that the characteristic resistance of the individual layers, the layer thicknesses relative to the cone diameter and the number of layers within the zone of influence of the cone affect the cone resistance in deposits containing multiple thin soil layers. Suggestions for practical implementation are also given.

Integrated study on the topographic and shallow subsurface expression of the Grote Brogel Fault at the boundary of the Roer Valley Graben, Belgium

The Grote Brogel Fault (GBF) is a major WNW-ESE striking normal fault in Belgium that diverges westward from the NW-SE striking western border fault system of the Roer Valley Graben. The GBF delimits the topographically higher Campine Block from the subsiding Roer Valley Graben, and is expressed in the Digital Terrain Model (DTM) by relief gradients or scarps. By integrating DTM, Electrical Resistivity Tomography (ERT), Cone Penetration Test (CPT) and borehole data, we studied the Quaternary activity of the GBF and its effects on local hydrogeology. In the shallow subsurface (<50m) underneath these scarps, fault splays of the GBF were interpreted on newly acquired ERT profiles at two investigation sites: one on the eastern section and the other on the western section, near the limit of the visible surface trace of the fault. Borehole and CPT data enabled stratigraphic interpretations of the ERT profiles and thereby allowed measuring vertical fault offsets at the base of Pleistocene fluvial deposits of up to 12m. Groundwater measurements in the boreholes and CPTs indicate that the GBF acts as a hydrologic boundary that prevents groundwater flow from the elevated footwall towards the hangingwall, resulting in hydraulic head differences of up to 12.7m. For the two investigation sites, the hydraulic head changes correlate with the relief gradient, which in turn correlates with the Quaternary vertical offset of the GBF. ERT profiles at the eastern site also revealed a local soft-linked stepover in the shallow subsurface, which affects groundwater levels in the different fault blocks, and illustrates the complex small-scale geometry of the GBF.

A hidden Markov random field model based approach for probabilistic site characterization using multiple cone penetration test data

This paper presents a new probabilistic site characterization approach for both soil classification and property estimation using sounding data from multiple cone penetration tests (CPTs) at a project site. A hidden Markov random field (HMRF) model based Bayesian clustering approach is developed, which can describe not only the heterogeneity of properties in statistically homogeneous soil layers, but also the correlation between spatial distributions of different soil layers. The latter has not been well considered in the existing CPT interpretation methods. A Monte Carlo Markov chain based expectation maximization (MCMC-EM) algorithm is adopted to calibrate the established HMRF model, so that both the subsurface soil/rock stratification and the pertinent soil properties can be estimated in a probabilistic manner. The proposed CPT interpretation approach is validated and demonstrated using a series of numerical examples, including using real CPT data. It is shown that the proposed method is able to accurately identify soil layers, pinpoint their boundaries, and provide reasonable estimates of the associated soil properties. In addition, comparative studies show that combining analysis of CPT data from multiple soundings, rather than interpreting them separately, can significantly enhance the accuracy of interpretation and simplify the subsequent task of interpreting stratigraphic profiles.

Interpolation of spatially varying but sparsely measured geo-data: A comparative study

Geological properties usually vary spatially, but they are often measured sparsely in engineering geology practice, particularly for projects with medium or relatively small sizes. Spatial interpolation is, therefore, frequently performed to estimate the geological properties of interest at unobserved locations. Among a variety of interpolation methods (e.g., ordinary kriging (OK), polynomial interpolation (PI), cubic spline interpolation (CSI) and inverse distance weighting (IDW)), OK is one of the widely used methods, and it requires modeling of spatial structure of the measurement data points using a semi-variogram. However, the accuracy of the semi-variogram is affected by data size, and it may give unreliable estimates when the measurement data are sparse. This poses a challenge in applications where the measurement data are sparse, such as site characterization. To address this challenge, Bayesian compressive sampling (BCS) has been recently developed, which is able to reconstruct a complete signal from a remarkably few number of measurement data points on that signal. Unlike OK, BCS does not require characterization of spatial structure from the available measurement data before the interpolation. Therefore, the difficulties associated with modeling a semi-variogram from sparse data in OK are bypassed in BCS. A comparative study is performed in this paper to compare the effectiveness of BCS with OK, PI, CSI and IDW using simulated data and real cone penetration test data. In addition, an approach is presented in this paper to objectively select the most appropriate basis function in BCS. It is found that the BCS method is more suitable and performs better than the OK, PI, CSI and IDW methods when the measurement data are limited and sparse, such as interpretation of geological data or site characterization data.

Probabilistic examination of the change in eigenfrequencies of an offshore wind turbine under progressive scour incorporating soil spatial variability

The trend for development in the offshore wind sector is towards larger turbines in deeper water. This results in higher wind and wave loads on these dynamically sensitive structures. Monopiles are the preferred foundation solution for offshore wind structures and have a typical expected design life of 20 years. These foundations have strict serviceability tolerances (e.g. mudline rotation of less than 0.25° during operation). Accurate determination of the system frequency is critical in order to ensure satisfactory performance over the design life, yet determination of the system stiffness and in particular the operational soil stiffness remains a significant challenge. Offshore site investigations typically focus on the determination of the soil conditions using Cone Penetration Test (CPT) data. This test gives large volumes of high quality data on the soil conditions at the test location, which can be correlated to soil strength and stiffness parameters and used directly in pile capacity models. However, a combination of factors including; parameter transformation, natural variability, the relatively small volume of the overall sea bed tested and operational effects such as the potential for scour development during turbine operation lead to large uncertainties in the soil stiffness values used in design. In this paper, the effects of scour erosion around unprotected foundations on the design system frequencies of an offshore wind turbine is investigated numerically. To account for the uncertainty in soil-structure interaction stiffness for a given offshore site, a stochastic ground model is developed using the data resulting from CPTs as inputs. Results indicate that the greater the depth of scour, the less certain a frequency-based SHM technique would be in accurately assessing scour magnitude based solely on first natural frequency measurements. However, using Receiver Operating Characteristic (ROC) curve analysis, the chance of detecting the presence of scour from the output frequencies is improved significantly and even modest scour depths of 0.25 pile diameters can be detected.

COMPRESSIBLE THICKNESS DEPTH DETERMINATION UNDER DIFFERENT CALCULATION METHODS OF THE SETTLEMENTS ACCORDING TO NATIONAL AND EUROPEAN STANDARDS

The comparison of the compressible thickness values and the settlements values according to the cone penetration test data in accordance with the current regulatory documents of the Belarus Republic and EUROCODE 7 «Geotechnical design» (part 1, 2) is presented. Two calculation methods of the foundation settlement for the limiting state of SLS and two methods for calculating of the settlements according to National standards are considered according to European norms. The ratios of the compressible thickness and the upset distances are determined using different calculation methods according to European and National standards. The proportions of the compressible thickness and the settlements values for different types of foundations are determined according to European and National standards.