Cone Penetration Test Measurements Research Articles

A new SPT-CPT correlation by statistical analysis based on a database from Anhui, China

ABSTRACT The standard penetration test (SPT) and cone penetration test (CPT) are the 2 most commonly used in situ tests in geotechnical investigations. In this study, various existing SPT–CPT correlations were assessed based on the database of Anhui Province, China, using data from 802 pairs of SPT–CPT measurements. The results showed that the existing correlations from other regions were not suitable for the geological conditions in Anhui Province. A new SPT–CPT conversion model based on the S curve was proposed through statistical analysis. This model introduced the soil behaviour index (Ic ) to SPT–CPT correlations to involve the influence of soil types on data interpretation. Comparative analysis demonstrated that the proposed correlation had the highest accuracy compared to correlations found in the literature, with the highest R2 value and lowest RMSE. Also, to validate the proposed model further, an additional set of investigation data was collected in Wuhu, Anhui, to verify the correlation. The correlation was successfully applied to predict a range of soil properties using the transformed N60 values. The validation analysis confirmed the highest accuracy of the correlation and its ability to effectively predict soil properties, thereby assisting in geotechnical design.

Correlation of CPT measurements and relative density of LEAP-2017 and LEAP-2020 centrifuge models

Miniature cone penetration tests (CPTs) were conducted during the LEAP 2017 and LEAP 2020 centrifuge experiments to characterize and quantify the soil deposit properties. A total of 43 CPTs were obtained for deposits with varying relative densities. All experiments were performed using Ottawa F-65 Sand and used the same CPT tool model. A comprehensive study was undertaken to assess the quality and consistency of the CPT data. Outlier and regression analyses were used to develop an empirical relation to evaluate the relative density of Ottawa sand (centrifuge) deposits from measurements of the CPT tip resistance. This relation provides an estimate of the mean relative density profile, and associated standard deviation to quantify uncertainty. The relation accounts for the overburden pressure influence and the potential effects of container width (when the used container is narrow). The experimental results were found to have a good consistency with a regression R2 equal to 0.85.

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.

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

The cone penetration test (CPT) is considered as one of the most reliable in-situ tests and has found numerous applications in the geotechnical engineering field. Traditional CPT interpretation includes, but are not limited to the identification of the soil stratification and the determination of soil parameters. This paper presents a case study concerning a test site located in Salzburg, Austria, in which we focus on the interpretation of CPTs from different perspectives. The manuscript is divided into three main sections dealing with three different aspects of CPT interpretation, namely stratification, ground variability and soil parameters. The first strategy introduces a machine learning based stratification identification strategy to detect soil layer boundaries from CPT measurements. A comparison with reference solutions demonstrates relative merits of this approach to classical filter algorithms based on empirical CPT classifications. The second strategy introduces an intuitive approach to evaluate the ground variability. This is achieved by calculating the level of fluctuation on the basis of CPT measurements and could be used as a data-driven decision-making tool for the improved design of CPT investigation layouts. The third strategy is embedded in an ongoing research project that aims to determine constitutive model parameters from in-situ tests using a graph-based methodology. In the present work, the developed automated parameter determination framework is applied to evaluate the soil parameters of one selected soil layer identified from the CPT interpretations. Potential lines of research in the context of CPT interpretation are explored throughout this work and may serve as valuable reference in future research.

Stochastic analysis of the seismic performance of sheet-pile walls supporting heterogeneous liquefiable ground

This paper presents the findings of a stochastic analysis conducted to evaluate the effects of the spatial soil variability and temporal base motion variability on the seismic response of sheet pile walls supporting liquefiable ground. A random finite element analysis is used to model the centrifuge experiment performed during the 2020 phase of the Liquefaction Experiments and Analysis Project (LEAP). The relative density of the main soil deposit in the LEAP-2020 experiments ranged from 50 to 75 %. In this study the spatial variability of each centrifuge model was determined from the inflight cone penetration tests (CPT) performed before the shaking phase in each experiment. The vertical spatial correlation length was directly determined from the CPT measurements, while the mean and coefficient of variation of the soil relative density were obtained from an empirical relationship correlating the cone tip resistance with the relative density. The variability observed in the achieved base motions for the LEAP-2020 centrifuge experiments was modeled by generating synthetic base motion time histories that matched the variability observed in the response spectra of the achieved base motions. The results obtained from this analysis shed light on the effects of the base motion variability and soil density on the excess pore pressure development and the triggering of soil liquefaction. It also provides an understanding on the sensitivity of the wall lateral displacement to such variabilities. The observed variability in the simulation response is compared with the variability observed in the centrifuge experiments. This comparison is used to evaluate the capability of the current numerical simulation tools in modeling the variability in the wall response.

Estimating geotechnical parameters using semisupervised learning: An example from the Dutch offshore wind farm zone

Wind energy is considered to be of great importance for promoting energy transition and achieving net-zero carbon emission. Reliable modeling and monitoring of the near-subsurface geology are crucial for successful wind farm selection, construction, operation, and maintenance. For optimal characterization of shallow seafloor sediments, 2D ultrahigh-resolution (UHR) seismic survey and 1D cone-penetration testing (CPT) often are acquired, processed, interpreted, and integrated for building 3D ground models of essential geotechnical parameters such as friction. Such a task faces multiple challenges, such as limited CPT availability, strong noise contamination in UHR seismic data, and heavy manual efforts for completing the traditional workflows, particularly acoustic impedance inversion. This study accelerates the integration by a semisupervised learning workflow with three highlights. First, it enables geotechnical parameter estimation directly from UHR seismic data without impedance inversion. The second comes from the use of a pretrained feature engine to reduce the risk of overfitting while mapping massive UHR seismic data with sparse CPT measurements through deep learning. More importantly, it allows incorporating other geologic/geophysical information, such as a predefined structural model, to further constrain the machine learning and boost its generalization capability. Its values are validated through applications to the Dutch wind farm zone for estimating four geotechnical parameters, including cone-tip resistance, sleeve friction, pore-water pressure, and the derived friction ratio, in two example scenarios: (1) UHR seismic data only and (2) UHR seismic data and an 11-layer structural model. The results verify the feasibility of data-driven geotechnical parameter estimation. In addition to the two demonstrated scenarios, our workflow can be further customized for embedding more constraints, e.g., prestack seismic and elastic/static property models, given their availability in a wind farm of interest.

Predicting subsurface classification in 2D from cone penetration test data

Uncertainty is inevitable in the characterisation of a geotechnical site, especially due to the inherently heterogeneous nature of the ground. In this paper, a method for characterising a subsurface with limited cone penetration test (CPT) data is proposed. The method is based on integrating predictions of CPT parameters with a probabilistic approach for subsoil classification at the CPTs. The predicted stratigraphy is able to capture the spatial variability of soil measured via CPTs and takes account of the uncertainties that arise from transforming CPT measurements into soil units as well as errors in the measurements themselves. The applicability of the proposed method is demonstrated for a site in the Netherlands. The results show that the proposed approach can identify the most likely classification in the domain with good accuracy. Furthermore, the significance of considering the uncertainties in predicting the most likely classification is illustrated via finite element stability analyses of a slope cut-out in the domain.

Probabilistic Liquefaction Assessment Based on an In-situ State Parameter Considering Soil Spatial Variability and Various Uncertainties

The state parameter (ψ) is based on a framework of critical state soil mechanics, which reflects the influences of soil compactness and stress level and has significant advantages for liquefaction analysis. In engineering practice, a simplified procedure based on the cone penetration test (CPT) to assess the liquefaction potential in the field involves model and parameter uncertainties. Furthermore, the use of limited CPT measurements involves project-specific test uncertainties, and the spatial variability of soil properties has a remarkable effect on soil liquefaction. To tackle these challenges, this study develops a ψ-based liquefaction probability framework to integrate the prior information of project-specific CPT, and uses limited tests to characterize the 2D spatial distribution of liquefaction potential with proper consideration of various uncertainties and soil spatial variability. The framework is developed based on simulation of Gaussian stationary random fields and Markov Chain Monte Carlo simulation. The proposed method is demonstrated using real CPT data from the 2011 Tohoku-Oki earthquake in Japan. The results indicate that the ψ-based evaluation performs well and can reasonably assess the liquefaction phenomenon of heterogeneous soils. This study incorporating the spatial variability of ψ into the liquefaction probability framework provides valuable information for geotechnical engineering design.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

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.

A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and Vs measurements

Evaluation of soil liquefaction potential is an essential step in geotechnical engineering design. This study presents a novel soil liquefaction potential evaluation system using cone penetration test (CPT) and shear wave velocity test (Vs) measurements. To this end, a new hybrid machine learning model called PSO-KELM model that combines the kernel extreme learning machine (KELM) with particle swarm optimization (PSO) is developed to assess soil liquefaction potential. Then, the PSO-KELM based searching technique is adopted to search the nonlinear relationship between CRR and CPT along with Vs measurements. Finally, a new probabilistic model is developed by considering the model uncertainty and sampling bias based on weighted maximum likelihood estimation. Results demonstrate that the performance of PSO-KELM model is significantly better than that of many other machine learning methods. The cyclic stress ratio, equivalent clean sand normalized cone tip resistance, normalized friction ratio, fines content, and soil behavior type are the recommended input variables for PSO-KELM model. The combined use of CPT and Vs measurements can significantly improve the prediction accuracy since it can more fully reflect soil liquefaction phenomenon. The proposed evaluation system can improve the performance of seismic CPT (SCPT) in soil liquefaction potential evaluation.

Application of High- and Low-Pass Spatial Filters in Analysis of Cone Penetration Test (CPTu)

The paper examines the high and low pass spatial filters used in the Cone Penetration Test (CPT) recordings in the soil and aims to demonstrate their usefulness and effectiveness in the interpretation of CPT measurements. The procedure of low-pass inverse filtration was used to reduce the effects associated with the occurrence of thin layers. High-pass filters were used to find places where the boundary of layers between different Soil Behaviour Type (SBT) could potentially exist. The application of both filters was tested on natural soils and on anthropological dumping soils.

Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

The continuous cone penetration test (CPT) measurements provide an advantageous liable rapid tool for stratification and soil behavior classification that can be employed in the sustainable design of the infrastructures. However, the CPT measurements are often interpreted by geotechnical experts because of the involved complexities and uncertainties. In this study, a novel stratification and soil type behavior (SBT) classification model is developed to identify the transition and thicker layers by integrating the geotechnical knowledge with the three submodels of (a) locally estimated scatterplot smoothing (LOESS), (b) a game theory model known as Nash–Harsanyi (N–H) bargaining, and (c) grey wolf optimizer (GWO). The LOESS and integrated N–H bargaining-GWO models are, respectively, used to approximate the outliers in CPT measurements and identify the SBT and layer changes. Attractively, in the proposed model, the engineer has the opportunity to judge on the precision of the stratification profile regarding their own preferences in a project. Solving simple algebraic equations, high speed, identifying thick and the interlayer transition layers, and small required training data are the other advantages of the developed model. Finally, the applicability of the proposed model has been assessed in an example. The compared estimated and two other models’ stratification profiles highlighted the potential of the proposed model to identify thin transition layers.

Reliability of CPT measurements in sand – influence of spacing

Geotechnical methods, such as cone penetration tests (CPTs), standard penetration tests (SPTs) and seismic cone penetration tests (sCPTs), are widely used to characterise the in situ properties of soil. In studies where more than one in situ method in close spacing is used, it is important for geotechnical engineers to balance between soil heterogeneity and the artefacts of disturbed testing zones. The same applies if detailed information about the subsoil is required and one in situ method is used in close spacing. There is no consensus on how to define the minimum spacing between testing zones and no study on the effect of disturbance caused by in situ tests. In this study, 33 CPTs were performed in natural sediments in northern Germany and a spacing threshold was defined at which cone resistance is affected by soil disturbance from previously performed CPTs. The CPTs were performed sequentially by successively refining the grid spacing, starting with a spacing of 119 cone diameters between CPTs, to a fine grid spacing of seven cone diameters. The cone resistance is affected by previous CPT measurements below a spacing threshold of 24 cone diameters in medium-dense sands. Silt and clay layers showed no reduction in the cone resistance for the minimum grid spacing of seven cone diameters.

Predicting land deformation by integrating InSAR data and cone penetration testing through machine learning techniques

Abstract. Built environments developed on compressible soils are susceptible to land deformation. The spatio-temporal monitoring and analysis of these deformations are necessary for sustainable development of cities. Techniques such as Interferometric Synthetic Aperture Radar (InSAR) or predictions based on soil mechanics using in situ characterization, such as Cone Penetration Testing (CPT) can be used for assessing such land deformations. Despite the combined advantages of these two methods, the relationship between them has not yet been investigated. Therefore, the major objective of this study is to reconcile InSAR measurements and CPT measurements using machine learning techniques in an attempt to better predict land deformation.

Shear Wave Velocity in the East Nile Delta Clay: Correlations with Static CPT Measurements

The East Port Said region, one of the most vibrant areas in the East Nile Delta, is a strategic economic zone that is receiving growing local and global investments. The near surface formations, however, typically comprise very soft clays deposited during the Holocene epoch. Collecting undisturbed samples and conducting laboratory evaluation of these sensitive soils could be cumbersome, inaccurate, and uneconomical. Therefore, the use of in-situ cone penetration test (CPT) provides a viable and convenient characterization alternative. This study attempts to establish site-specific correlations between the measurements of the static CPT and shear wave velocities (Vs) furnished by seismic piezocone soundings. The mathematical regressions were developed utilizing a number of measurements and factors, namely, tip resistance, sleeve friction, effective stress, depth, voids ratio, and soil behavior type index. The proposed correlations demonstrate different levels of predictability, with a few warranting confidence in employing static CPT for estimation of Vs profile in this region. Two correlations, one utilizing void ratio and tip resistance and the other employing sleeve friction solely, have shown to provide the most reliable Vs predictions for the soft clay in the East Nile Delta.

An Experimental Study on Triaxial Compression Tests and Cone Penetration Tests in Planetary Regolith Simulant under Low Gravity Fields

Abstract Lunar exploration projects aiming to develop space exploration of mineral resources outside of Earth have been relaunched recently by many countries together with commercial partnerships. The fundamental understanding of the mechanisms related to the low gravity fields and specific planetary regolith is therefore the key to further plans for site investigation, resource utilization, and outpost construction. The triaxial compression test is a conventional method for measurements of mechanical properties of various granular materials, whereas the cone penetration test (CPT) could serve as an effective in situ tool for site investigation in deep space exploration. In this article, the geotechnical magneto-gravity model testing method is adopted to conduct triaxial tests and CPTs in a type of magnetic regolith simulant, under various low gravity fields. Triaxial testing data indicate the influence of g-level on both stress–strain curves and shear strength characteristics, as well as the independence of critical-state indexes at failure. The interpretation of CPT data under low gravity fields is then investigated, showing that the correlations between soil properties and CPT measurements require modifications for the application of in situ tests in the near-earth space exploration. The findings of this study provide some insights into the mechanics under various gravity-induced stress gradients and potentially contribute to further developments on surface excavation, base construction, and resource exploitation.

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.

Empirical SPT-CPT correlation for soils from Lahore, Pakistan

Standard Penetration Test (SPT) and cone penetration test (CPT) are the most widely used in situ tests to depict the soil stratigraphy and determine the geotechnical engineering of the subsurface soils. The CPT leaps out because of its capability to trace the resistance continuously and due to its accuracy, it is considered to be reliable than the SPT. The outcome of these tests is very important for the design procedures to be implemented for various geotechnical purposes. To effectively utilize all the available data, there is a need of updating the correlation between these two widely used in situ tests. This study implements the statistical linear regression model using a 107 SPT and 49 CPT measurements across the city of Lahore, Pakistan to develop correlations between the the uncorrected SPT blow counts (N) and cone resistance (qc) for various soils. The developed correlation is compared in terms of qc/N ratios with the previous published studies.

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.