Cone Penetration Test Data Research Articles

Prediction of Shear Wave Velocity in Fine-grained Soils From Cone Penetration Test Data: Toward a Global Approach

Shear wave velocity ([Formula: see text]) is often used for the calculation of the shear modulus to combat sampling issues in clays and minimize the cost of laboratory testing. Several studies reported correlations between [Formula: see text] and direct and indirect measurements of the cone penetration test (CPT), yet those proposed for fine-grained formations are generally of a local nature. This study sets out to propose a global approach for the estimation of [Formula: see text] for these soils. Seven regressions were developed using a total of 632 CPT data measurements collected from 55 different sites in 12 countries. The correlations utilizing the tip resistance and depth statistically outperform those using other CPT data. A total of 165 CPT points—collected from the state of New Jersey, USA, and Mexico City, Mexico—were used for verification. It is instrumental to analyze CPT data before building global correlations for gauging applicability and understanding limitations. The adopted approach paves the way for more methodical and rigorous global regressions.

An initiative to correlate the SPT and CPT data for an alluvial deposit of Dhaka city

The Standard Penetration Test (SPT) is the most common in-situ tests for soil investigations. On the other hand, the Cone Penetration Test (CPT) is considered one of the best investigation tools. Many CPT-SPT correlation relationships have been proposed worldwide to estimate soil parameter from the other's available data. However, unfortunately, no available SPT-CPT correlations are established for the alluvial soil of Dhaka city. This paper aims at presenting correlations among the SPT-N value, cone tip resistance (qc), sleeve friction resistance (fs), soil behavior index (Ic) and mean particle size (D50) for an alluvial soil deposit of Dhaka city. It is found that for the relationship between equivalent SPT N60-value and SPT N60-value the coarser soil layers As1 and As2 show the coefficient of correlation (R2) is 0.7106 and 0.534 respectively, which indicate a reliable relationship. In addition, the correlation between cone tip resistance (qc) and SPT N60-value shows very strong relationship which is very similar to proposed Meyerhof correlation. Furthermore, the relations between other mentioned parametres also shows a valid correlations similar to other authors.

Developing multivariate adaptive regression splines model for predicting the undrained shear strength of clayey soil from cone penetration test data

Developing multivariate adaptive regression splines model for predicting the undrained shear strength of clayey soil from cone penetration test data

Benchmark examples for data-driven site characterisation

ABSTRACT Decision making in geotechnical engineering is always related to a project carried out at a specific site. It is natural for data-driven site characterization (DDSC) to attract the most attention in data-centric geotechnics. This paper proposed eight benchmark examples and a benchmarking procedure to support unbiased and competitive evaluation of emerging ML methods. The primary goal of DDSC is to bring the value of a “data first” agenda to practice, specifically to produce a 3D stratigraphic map of the subsurface volume below a full-scale project site and to estimate relevant engineering properties at each spatial point based on site investigation data and other relevant Big Indirect Data (BID). A reasonable full-scale ground 20 m long × 20 m wide × 10 m deep is adopted. Virtual grounds containing horizontal, inclined, or discontinuous soil layers and spatially varying synthetic cone penetration test data are created to test the performance of DDSC methods over a range of ground conditions. A benchmark example is defined by a combination of a virtual ground (“reality”) and a training dataset (measured “reality”). An additional benchmark example based on actual CPT data is included to check whether performance under virtual ground conditions holds under real ground conditions.

Assessment of Negative Skin Friction in Bored Cast-In-Situ Piles According to Cone Penetration Test Data in Subsiding Soils

Assessment of Negative Skin Friction in Bored Cast-In-Situ Piles According to Cone Penetration Test Data in Subsiding Soils

FOUNDATION CHARACTERIZATION IN IKATE AREA SOUTHWESTERN NIGERIA USING ELECTRICAL RESISTIVITY TOMOGRAPHY AND CONE PENETRATION TEST

The establishments of the nexus between the underlying geology and engineering properties are fundamentals to the safe, economic and serviceable designs of civil engineering structures. Cone Penetration Test (CPT) and Electrical Resistivity Tomography (ERT) are critical tools in foundation parameterization especially within Quaternary sediments serving as foundation of engineering structures. Both techniques were used to obtained point data and lateral continuity of geologic units within Ikate Area of Lagos Metropolis for foundation parameterization. Measurements which comprising 4 profiles of 2D resistivity imaging using the Wenner electrode configuration; 1 borehole and 12 cone penetration tests (CPT) were taken with Pasi Terrameter, percussion rig and the 10.0 tons penetrometer respectively. The ERT results interpreted using Dipro software were presented in Pseudo-sections while the cone penetration test results analysed with Microsoft Excel were represented as resistance curves. Interpretation from the two methods integrated with the borehole results reveals the presence clay, peat and sand of varying properties. The uppermost layer consists of medium dense sand followed by clay/peat of very low resistivity (< 3.5 ohm-meter) covering about 6. 0 – 8.0m in depth and terminating in another layer of sand (8.0 – 15.0m). The last sandy layer with cone resistance range between 30 – 120kg/cm2 is considered a suitable layer upon which deep foundation may be founded. Shallow foundation capacity estimates at depths 0.2 – 4.0m derived from direct CPT data using three different computation techniques gives value range from 1.7 – 49.4kg/cm2, 0.7 – 15.2kg/cm2 and 0.4 – 12.6kg/cm2 respectively. Deep foundation for skyscrapers within the area are advised to be founded within the competent layer encountered beyond 10.m depth.

Interpretation of Cone Penetration Test Data of an Embankment for Coupled Numerical Modeling

The Nerang Broadbeach Roadway (NBR) embankment in Australia is founded on soft clay deposits. The embankment sections were preloaded and surcharged-preloaded to limit the post-construction deformation and to avoid stability failure. In this paper, we discuss the NBR embankment’s geology, geotechnical properties of the subsurface, and long-term field monitoring data from settlement plates and piezometers. We demonstrate a comparison of cone penetration test (CPT) and piezo cone dissipation test (CPT-u) interpreted geotechnical properties and the NBR embankment’s foundation stratification with laboratory and field measured data. We also developed two elasto-viscoplastic (EVP) models for long-term performance prediction of the NBR embankment. In this regard, we considered both the associated and the non-associated flow rule in the EVP model formulation to assess the flow rule effect of soft clay. We also compared EVP model predictions with the Modified Cam Clay (MCC) model to evaluate the effect of viscous behavior of natural Estuarine clay. Both EVP models require six parameters, and five of them are similar to the MCC model. We used the secondary compression index of clay in the EVP model formulations to include the viscous response of clay. We obtained numerical models’ parameters from laboratory tests and interpretation of CPT and CPTu data. We observed that the EVP models predicted well compared with the MCC model because of the inclusion of soft clay’s viscosity in the EVP models. Moreover, the flow rule effect in the embankment’s performance predictions was noticeable. The non-associated flow rule EVP model predicted the field monitoring settlement and pore pressure better compared to the MCC model and the associated flow EVP model.

Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data

This research investigates and compares the performance of three tree-based Machine Learning (ML) methods, Canonical Correlation Forest (CCF), Rotation Forest (RotFor), and Random Forest (RF), for predicting the liquefaction potential of soils based on the cone penetration test (CPT) case history datasets collected from previously published research. The ML models are trained and validated using the Stratified Random Sampling technique for training and test datasets considering three sampling ratios as 50:50, 40:60, and 70:30. In addition, a comparative example was applied to show the difference between the Stratified Random Sampling and the Simple Random Sampling technique, which is the most common probability-based sampling method, considering only a dataset. The predictive capabilities of the developed models are evaluated using Overall Accuracy, Kappa, Precision, Recall, and F-Measure values. Lastly, the Wilcoxon Signed-Rank Test and the Pearson's Correlation Coefficient are adopted to determine the statistical significance of the accuracies between the tree-based ML methods. Generally, tree-based ML methods of CCF, RotFor and, RF are found robust with respect to the variations in training sample sizes, and the performance metrics revealed that the CCF and RotFor method exhibited slightly better performance than the conventional RF method. Finally, based on the results obtained from performance assessment output, CCF and RotFor methods which are the first application in the soil liquefaction issue to the best of our knowledge are worth considering in the prediction of soil liquefaction.

Use of Cone Penetration Test for Classification of Some Sudanese Soils; the State of the Art

The cone penetration test (CPT) method has successfully been applied in Sudan for research, foundation design and the solution of geotechnical problems for various engineering structures since 1977. Based on analyses of database accumulated during four decades correlation relationships have been developed to classify local soils from CPT data. This paper presents the state of the art and the main findings of previous studies carried out on the application of CPT for soil classification. A simple and reliable CPT method based on the statistical “discriminant analysis” technique was proposed in 1980 to classify a given soil of known CPT parameters into one of four main USCS soil groups. Another correlation was proposed in a different study between CPT data andrelative consistency of highly plastic clay soils. The original soil classification method was further modified in 2003 to include more database and incorporate additional features relating to the consistency of clay soils and relative density of sandy soils. The accuracy of the CPT based soil classification method was examined amongst worldwide known methods by comparison of the soil types predicted from CPT and the actual soil profiles. The comparison study showed that the method performed quite well in classifying alluvial soils which are similar to those used in their development.

System Response of an Interlayered Deposit with Spatially Preferential Liquefaction Manifestations

The Canterbury Earthquake Sequence produced a spatial pattern of liquefaction-induced surface ejecta at an open field along Palinurus Road in Christchurch, New Zealand, that would not be expected based on simplified liquefaction evaluation procedures. Half the site discharged sand boils and the other half did not. Two-dimensional fully-coupled nonlinear dynamic analyses (NDAs) were performed to examine why simplified one-dimensional liquefaction vulnerability indices (LVIs) overestimated liquefaction manifestations at this site for the 2010 Darfield and 2011 Christchurch earthquakes and did not distinguish between areas with and without surface ejecta. The NDAs use the PM4Sand and PM4Silt constitutive models for sand-like and clay-like portions of the subsurface, respectively, within the Fast Lagrangian Analysis of Continua (FLAC) finite-difference program. Material parameters are obtained from in situ geophysical and cone penetration test (CPT) data. A sensitivity study is performed to assess the influence of (1) representative soil property selections and the use of a CPT inverse filtering procedure to correct for thin-layer and transition zone effects, (2) ground motions developed by two distinct methods (i.e., recordings and physics-based simulations), and (3) model assumptions affecting diffusion during reconsolidation. Ground deformations and flow patterns during and after ground shaking are examined. The results provide insights on how stratigraphic details and other factors can affect the system response and dictate the degree and extent of liquefaction surface manifestations.

Spatial variability characteristics of the effective friction angle of Crag deposits and its effects on slope stability

This study investigated the spatial variability characteristics of the effective friction angle of Crag deposits which are granular soils occur in the east of England. Cone Penetration Test data were obtained at 26 locations and interpreted statistically. The distribution characteristic of the effective friction angle of Crag deposits was derived with the mean value, the standard deviation and the correlation length calibrated. Illustrations were also shown on how factors such as ground water pressures and the existence of soft/organic soil zones affect the measurement of the autocovariance function and thus the correlation length. Bayesian inference technique was adopted alongside the method of moments to determine the correlation length. Based on the obtained statistical parameters, both semi-deterministic (based on standard geotechnical design codes) and probabilistic finite element limit analyses were carried out to investigate the stability of slopes in Crag deposits. Slopes of various inclined angles were considered and comparisons between the semi-deterministic and probabilistic results were conducted to improve the understanding of the stability of Crag slopes and to provide insight into the slope stability code used in practice.

A multiple model machine learning approach for soil classification from cone penetration test data

The most popular methods for soil classification from cone penetration test (CPT) data are based on examining two-dimensional charts. In the last years, several authors have dedicated efforts on replicating and discussing these methods using machine learning techniques. Nonetheless, most of them apply few techniques, include only one dataset and do not explore more than three input features. This work circumvents these issues by: (i) comparing five different machine learning techniques, which are also combined in an ensemble; (ii) using three distinct CPT datasets, one composed of 111 soundings from different countries, one composed of 38 soundings with information of soil age and the third composed of 64 soundings taken from the city of São Paulo, Brazil; and (iii) testing combinations of five input features. Results show that, in most cases, the ensemble of multiple models achieves better predictive performance than any technique isolated. Accuracies close to the maximum were obtained in some cases without the need of pore pressure information, which is costly to measure in geotechnical practice.

Evaluation of potential liquefaction based on Cone Penetration Test (CPT) data

An earthquake can inflict a liquefaction hazards which can damage buildings and infrastructure. Furthermore, earthquakes are difficult to predict when and where earthquakes will occur and happen suddenly without being preceded by signs. Therefore, we must do a geological investigation before building a construction to evaluate the potential liquefaction in that area. Evaluation of the potential liquefaction using Cone Penetration Test (CPT) is one method can be used because repeatable, provided a continuous profile, and economic. This method needs peak ground acceleration (amax) value at an interval of 0.1 g to 0.6 g and earthquake magnitude of 6.2 scale richter. Based on the results of the research was obtained in this research area, there were potential liquefactions when the peak ground acceleration (amax) value was above 0.3 g.

Case Histories on 2D/3D Underground Stratification Using Sparse Machine Learning

This paper showcases novel underground stratification based on sparse machine learning (SML) methods, including sparse Bayesian learning (SBL) and least absolute shrinkage selection operator (Lasso). The SML methods proposed by the authors were applied to two- and three-dimensional underground stratification of actual sites, Odagawa Riverbank (Okayama, Japan) and New Lock (Terneuzen, the Netherlands), to demonstrate their performances. Cone penetration test (CPT) data were available in both sites, and they were converted to the soil behavior type (SBT) index for the underground stratification analysis. The trends of Ic/SBT profiles and their distribution colormaps estimated by the two methods were compared to discuss the methodological characteristics. For the Odagawa Riverbank case, the detection ratio of SBT obtained by the two methods was also compared to investigate the estimation accuracy in terms of stratification ability.

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.

Robust identification and characterization of thin soil layers in cone penetration data by piecewise layer optimization

Cone penetration testing (CPT) is a preferred method for characterizing soil profiles for evaluating seismic liquefaction triggering potential. However, CPT has limitations in characterizing highly stratified profiles because the measured tip resistance (qc) of the cone penetrometer is influenced by the properties of the soils above and below the tip. This results in measured qc values that appear “blurred” at sediment layer boundaries, inhibiting our ability to characterize thinly layered strata that are potentially liquefiable. Removing this “blur” has been previously posed as a continuous optimization problem, but in some cases this methodology has been less efficacious than desired. Thus, we propose a new approach to determine the corrected qc values (i.e. values that would be measured in a stratum absent of thin-layer effects) from measured values. This new numerical optimization algorithm searches for soil profiles with a finite number of layers which can automatically be added or removed as needed. This algorithm is provided as open-source MATLAB software. It yields corrected qc values when applied to computer-simulated and calibration chamber CPT data. We compare two versions of the new algorithm that numerically optimize different functions, one of which uses a logarithm to refine fine-scale details, but which requires longer calculation times to yield improved corrected qc profiles.

Novel approach to estimate vertical scale of fluctuation based on CPT data using convolutional neural networks

The inherent spatial variability of soil properties is the main sauces of uncertainties in the site investigation, and it is commonly characterized using random field theory. In the context of random fields, the scale of fluctuation (SOF) is a significant parameter to reflect the spatial correlation between the two points of soil properties. However, it is challenging to estimate the SOF value accurately, especially when there are only limited project-specific test results, such as cone penetration test (CPT) data. This study aims to develop a convolutional neural network (CNN) approach to estimate the vertical SOF based on limited CPT data. The CNN model was constructed and trained by the simulated 15,000 CPT samples using random fields. The results show that the CNN model has excellent performance for estimating vertical SOF. The approach is validated and illustrated through newly simulated CPT data, eight real CPT data obtained from the literature, and three CPT data collected from the Shanghai site. The proposed scale factor method can solve the mismatch between the actual CPT depth and the required depth for input data of the CNN model, making the CNN model more widely applicable.

Modified refracted ray path method for determination of shear wave velocity profiles using seismic cone

The commonly used data reduction methods to determine shear wave velocity (Vs) require the depth interval of the Vs profile to be the same as the in-situ testing interval. The Vs values derived in this way are the mean values within the depth intervals. However, the in-situ multilayer interfaces do not always coincide with the predetermined testing depths, and the materials may change dramatically across layers and thus the above interpretation methods may not always produce representative soil properties. In this study, a modified refracted ray path method for more flexible Vs calculation is proposed. The method takes advantage of the routine cone penetration testing data obtained in SCPT tests and applies a combined approach for the soil layer identification using both the corrected cone tip resistance (qt) and the Soil Behavior Type Index (Ic) as indicators. Vs is then calculated using the Snell's law based on the original shear wave travel times and the newly identified layer profiles. The proposed method can avoid the error caused by crossing the multilayer boundaries and provide a more accurate prediction of soil type and the corresponding stiffness properties. The data show that by using the proposed method, the accuracy of the estimated Vs can be improved by up to 77% for a soil layer with interface not located at testing depths, as compared to Vs obtained from the conventional methods.

Artificial neural network for prediction of liquefaction triggering based on CPT data

The prediction of the liquefaction potential of soil due to an earthquake is an essential task in civil engineering. In this paper, the artificial neural networks (ANNs) technique is introduced in the prediction of liquefaction potential of soil based on the cone penetration test (CPT) data. ANNs model was developed and validated using a database of 174 field case histories. Six parameters were assigned as input parameters of the model which were earthquake magnitude (M), effective vertical stress (☐′), cone resistance (qc), normalized peak horizontal acceleration at the ground surface (☐/g), soil mean grain size (D50), and cyclic stress ratio (CSR). The output of the model was liquefaction index (LI) which in turn was used to determine whether liquefaction was taking place or not. The developed ANN model gave well-matched results when compared with the actual results. Also, the study for the relative importance of the input parameters was performed. It showed that qc and M exhibited the highest importance of approximately 33% and 23% respectively while the value of (☐/g) yielded the lowest value of 9.7%. Finally, based on the sensitivity analysis of the model, it was found that the results of the ANN model were compatible with prior geotechnical knowledge. Accordingly, it can be concluded that neural networks can be used to simulate the problem of soil liquefaction with high accuracy.

Analysis of Bored Pile Foundation Bearing Capacity Based on Cone Penetration Test Data (Case Study: Cilellang Weir Location)

The use of a bored pile foundation is an alternative in planning deep foundations. The function of this bored pile foundation is more or less the same as other deep foundations such as piles but has a slight difference in the process. The bored pile foundation begins with drilling the ground to the planned depth, followed by the installation of steel reinforcement and then concrete mortar. This study aims to evaluate the Cone Penetration Test (CPT) data on the bearing capacity of the bored pile foundation. Calculation of bearing capacity from Cone Penetration Test (CPT) data using the Schmertmann & Nottingham method and the Mayerhof method. Based on CPT data, the percentage calculation of ultimate bearing capacity at location S1 with the Schmertmann & Nottingham method is more optimistic ±17.28% compared to using the Mayerhof method for bored pile diameters of 60 and 80 cm, while for bored pile diameters of 40 cm, calculations using the Mayerhof method show more optimistic by 21.89%. The percentage calculation of ultimate bearing capacity at location S2 using the Schmertmann & Nottingham method is ±11.66% more optimistic than using the Mayerhof method for bored pile diameters of 60 and 80 cm, while the diameter of the bored pile 40 cm, the calculation using the Mayerhof method shows a more optimistic result of 33.82%.