Cone Penetration Test Data Research Articles

Spatial and correlation analysis of engineering-geological survey data for logistics center construction

Introduction: at present, the geostatistical methodology is broadly used abroad for constructing spatial-correlation and spatial-stochastic models of lithoengineering systems, including description and analysis of soil body heterogeneity. The main goal of this work is an attempt to evaluate the possibility of generating a spatial-correlation model of lithoengineering space based on survey data which could be used for subsequent simulation and deterministic-stochastic analysis of geotechnical structures as well as when designing bases and foundations. Materials and methods: main input parameters for spatial analysis were geological survey report and cone-penetration test (CPT) data. The data were analyzed using descriptive statistic methods, with the calculation of particular and ambiguous values and using statistical software STATISTICA. Later use of the results of the statistical analysis aims at the application of geostatistical interpolation models (kriging) for generating spatial structures of deformation features. The article elaborates experimental correlation functions (variograms) to validate the developed spatial structures. The functions show not only the validity of the spatial simulation but also a level of heterogeneity of the researched soil body. Results: the statistical analysis of the initial data showed relative homogeneity of the researched soil body properties what is substantiated by GOST 20522 instructions. Nevertheless, the geostatistical analysis resulted in the opposite conclusion. Principally, this is due to the lack of information on the researched geological medium. Conclusions: requirements of current Russian standard GOST 20522 do not contain a sufficient amount of information for geostatistical analysis and simulation of a soil body based on modern geostatistical methods. As a result, data of standard engineering-geological researches cannot be used for developing digital foundation models as well as for using probabilistic approaches in geotechnics.

Towards consideration of epistemic uncertainty in shear-wave velocity measurements obtained via seismic cone penetration testing (SCPT)

Seismic cone penetration testing (SCPT) is a powerful geotechnical site characterization tool, allowing for simultaneous collection of routine cone penetration testing data and rapid downhole-type measurements of shear-wave velocity (VS). However, the uncertainties associated with developing VS profiles from SCPT measurements are rarely considered or communicated to the end-user. One important source of VS uncertainty is related to how the shear wave travel times are interpreted from the recorded waveforms, while another critical source of uncertainty is related to the analysis method used to transform the travel times to velocities. In this study, four common ways of obtaining travel times were considered: (i) first arrival picks, (ii) peaks and troughs picks, (iii) crossover picks, and (iv) the peak response of the cross-correlation function. Using these different travel times, a number of VS profiles were developed using four different velocity analysis methods: (i) pseudo-interval, (ii) true-interval, (iii) corrected vertical travel time slope-based, and (iv) raytracing. Through consideration of multiple wave arrival time and velocity analysis methods, a robust and meaningful quantification of the intramethod, depth-dependent epistemic uncertainty in VS obtained from several example SCPT datasets has been developed. VS uncertainty is further examined through consideration of the intermethod variability and bias between SCPT and direct-push crosshole testing.

Sample size determination in geotechnical site investigation considering spatial variation and correlation

Site investigation is a fundamental element in geotechnical engineering practice, but only a small portion of geomaterials is sampled and tested during site investigation. This leads to a question of sample size determination: how many samples are needed to achieve a target level of accuracy for the results inferred from the samples? Sample size determination is a well-known topic in statistics and has many applications in a wide variety of areas. However, conventional statistical methods, which mainly deal with independent data, only have limited applications in geotechnical site investigation because geotechnical data are not independent, but spatially varying and correlated. Existing design codes around the world (e.g., Eurocode 7) only provide conceptual principles on sample size determination. No scientific or quantitative method is available for sample size determination in site investigation considering spatial variation and correlation of geotechnical properties. This study performs an extensive parametric study and develops a statistical chart for sample size determination with consideration of spatial variation and correlation using Bayesian compressive sensing or sampling. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical chart, and the method is shown to perform well.

Stratigraphic model of the Quaternary sediments of the Western Irish Sea Mud Belt from core, geotechnical and acoustic data

A new geotechno-stratigraphic model for Quaternary deposits in the Western Irish Sea Mud Belt area is presented. This area, located in the North Irish Sea, has been heavily influenced by the advance and retreat of the British and Irish Ice Sheet during the last glaciation and subsequent Holocene marine transgression. This study uses a synthesis of lithostratigraphy, geotechnical data (from in situ cone penetration testing) and seismic profiles to generate a stratigraphic framework describing the geometry, distribution and characteristics of Quaternary deposits in an area of the Western Irish Sea Mud Belt at increased resolution. Within a regional context, this stratigraphy is compared with other established offshore stratigraphies in the Irish Sea and terrestrial successions. Four stratigraphic units are identified consisting of a basal subglacial (lodgement) till (Unit 4) emplaced by the Irish Sea Ice Stream. Unit 4 overlies irregular bedrock and is overlain in turn by glaciomarine to glaciolacustrine, ice-proximal outwash and glaciomarine ice-proximal sand and sandy muds (Units 3 and 2 respectively; previously undifferentiated). A thick succession (up to 27 m) of Holocene marine muds (Unit 1) caps the sequence up to the contemporary seabed. Results show that these deposits, and their geotechnical properties, have significant implications for the anthropogenic use of the area, such as the development of offshore renewable energy infrastructure. Revealed are relatively thick (up to 30 m) weak, under-consolidated sediments at the surface and highly heterogeneous, often over-consolidated, sediments that have limited groundtruthing at depth. Furthermore, localised shallow gas is imaged on seismic profiles. The influence on the geotechnical properties of the sediments by this gas revealed no significant effect on shear strength values from cone penetration testing data, although it may have implications for the long-term behaviour of the sediments.

On the need for dependence characterization in random fields: Findings from cone penetration test (CPT) data

Random field theory is widely used to model spatial variability of soil properties. However, random field modeling focuses mainly on the estimate of spatial correlation structure. The dependence structure that is necessary to construct the joint probability distribution over a random field is usually not characterized. The aim of this research is twofold. First, this paper focuses on characterizing the dependence structure underlying a random field based on cone penetration test (CPT) data. The copula approach is adopted to represent dependencies and the best-fit dependence (copulas) are identified from the CPT data. It is found that the nonGaussian dependencies can be a real phenomenon in spatial fluctuation of the soil shear strength parameter. Second, this paper provides formulations for generating random fields with Gaussian or nonGaussian dependencies, and investigates whether the improper use of the dependence structure could lead to significant bias in failure probability. The generated one-dimensional (1-D) and two-dimensional (2-D) random fields of a cohesive slope under different dependencies are compared. Large deviation in probabilistic results implies that the effect of dependencies on failure probability can be nontrivial. Therefore, the complete random field characterization should involve the estimate of both correlation structure and dependence structure.

A Method for Predicting Pile Capacity Using Cone Penetration Test Data

The massive construction in poor lands has encouraged engineers to use deep foundations in order to transfer superstructure loads to the subsoil. Since soil excavation, sampling, and laboratory testing as a part of site investigation are relatively difficult, in-situ tests such as cone penetration test (CPT) as a very informative test may be recommended. The CPT has been widely used in engineering as a part of site investigation, and its data has been used to determine the axial capacity of piles. In this paper, the prediction capability of three empirical widely famous old methods used to predict the axial pile capacity based on CPT data is evaluated by using field data obtained from direct field pile loading tests. In this evaluation, the direct pile load test results are used as measured data. Three popular famous statistical evaluation methods namely the best-fitted line, geometric mean, and geometric standard deviation have been used. The evaluation results indicate that generally although predicting methods based on CPT data have been widely used to determine the axial bearing capacity of piles, they need to be upgraded for the economic and relatively accurate design of piles. According to the statistical studies carried out in the current research, among three old empirical methods, although the Nottingham and Schmertmann Method (1975, 1978) (NSM) [7, 8] has the best agreement with test results, it is felt that the method needs to be upgraded. The modification of NSM has been done in the current paper using a comprehensive database.

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.

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.

Bayesian identification of soil stratigraphy based on soil behaviour type index

The cone penetration test (CPT) has been widely used to determine the soil stratigraphy (including the number N and thicknesses HNof soil layers) during geotechnical site investigation because it is rapid, repeatable, and economical. For this purpose, several deterministic and probabilistic approaches have been developed in the literature, but these approaches generally only give the “best” estimates (e.g., the most probable values) of N and HNbased on CPT data according to prescribed soil stratification criteria, providing no information on the identification uncertainty (degrees-of-belief) in these “best” estimates. This paper develops a Bayesian framework for probabilistic soil stratification based on the profile of soil behaviour type index Iccalculated from CPT data. The proposed Bayesian framework not only provides the most probable values of N and HN, but also quantifies their associated identification uncertainty based on the Icprofile and prior knowledge. Equations are derived for the proposed approach, and they are illustrated and validated using real and simulated Icprofiles. Results show that the proposed approach properly identifies the most probable soil stratigraphy based on the Icprofile and prior knowledge, and rationally quantifies the uncertainty in identified soil stratigraphy with consideration of inherent spatial variability of Ic.

Validating land-based FDEM data and derived conductivity maps: Assessment of signal calibration, signal attenuation and the impact of heterogeneity

Frequency domain electromagnetic (FDEM) responses can be related to electrical conductivity (EC) and magnetic susceptibility of the subsurface. Strides in FDEM instrumentation and EM modelling drive the rapid growth of land-based applications. However, the ease of use of FDEM instruments is starkly contrasted by data complexity and laborious calibration. When facing high conductivities, linear approximations between the quadrature phase and EC are no longer valid and the depth of investigation (DOI) is uncertain, complicating an accurate delineation and characterization of the subsurface. To assess the signal attenuation, a simple method for the calculation of the DOI was established and to explore the FDEM calibration and its potential for accurate conductivity mapping in a high conductivity setting, a land-based survey was conducted using a custom approach. The tailored approach relied on the collection of multi-configuration FDEM data as well as cone-penetration testing (CPT) data (with a mounted EC module). All data were collected in a two day time span to ensure maximum correspondence of all (temperature and moisture dependent) data. The CPT-EC data resulted in discrete profiles with a high vertical resolution and the FDEM data allowed good area coverage. The CPT conductivity logs were then used to calculate theoretical instrument responses (to evaluate FDEM data uncertainty or offsets) and obtain optimal inversion parameters. An excellent correlation between the forward modelled CPT-EC and measured FDEM data was uncovered, advocating the use of the applied forward modelling routine and the combination of CPT-EC and FDEM. Unfortunately, the presence of large discrepancies in the comparison data impeded the use for levelling purposes. This was likely the result of the erroneous assumption of lateral homogeneity made during the 1D forward modelling, demonstrating the large impact discrete, contrasting subsurface bodies can have. Following, the FDEM data were inverted to assess the accuracy potential.

Estimation of autocorrelation distances for in-situ geotechnical properties using limited data

A simultaneous estimation of the vertical and horizontal autocorrelation distances (ACDs) for geotechnical properties under the condition of limited data (fewer than one hundred data points) is not well studied. This paper evaluates the performance of the maximum likelihood estimator (MLE) in estimating autocorrelation distances using some simulated data. The effect of the autocorrelation structure on the accuracy of ACD estimation is also investigated. It is found that MLE may produce highly biased estimations if limited data are available. Hence, a clustered borehole layout scheme and a resampling method are proposed to improve the estimation accuracy. The methods are illustrated using some simulated data, standard penetration test (SPT) data and cone penetration test (CPT) data. It is found that the clustered borehole layout scheme and resampling method yield more accurate estimations of ACDs in analyses using both the simulated data and real data.

Seasonal influence on cone penetration test: An unsaturated soil site example

Interpretation of electric cone penetration test (CPT) based pore water pressure measurement (CPTu) is well established for soils with behavior that follows classical soil mechanics. The literature on the interpretation of these tests performed on unsaturated tropical soils is limited, and little is known about the influence of soil suction on in situ test data. In this context, the CPT data are presented and discussed to illustrate the seasonal variability in an unsaturated tropical soil site. The test data show that soil suction significantly influenced CPT data up to a depth of 4 m at the study site. It shows the importance of considering seasonal variability in unsaturated soil sites caused by soil suction, which was related to water content through a soil-water retention curve (SWRC). It is also important to consider this aspect in the interpretation of CPT data from these soils.

Correlations of SPT, CPT and pressuremeter test data in alluvial soils. Case study: Tabriz Metro Line 2, Iran

The Menard pressuremeter modulus (Em), limit pressure (PL), shear modulus (G) and subgrade reaction modulus (Ks) can be obtained by using the pressuremeter test, and are needed as parameters to obtain the geotechnical parameters for foundation design and to determine the ultimate bearing capacity in engineering projects. In this article, correlations of Menard pressuremeter tests (PMTs) with standard penetration tests (SPTs) and cone penetration tests (CPTs) are obtained as a consequence of the extensive geotechnical investigation performed in alluvial soils of the Tabriz Metro Line 2, Iran. A total of 85 boreholes with depths of 20–40 m were drilled, and the three mentioned tests were performed at varying depths. Correlations were carried out separately for silty clay (CL), clayey silt (ML) and silty sand (SM) soils, respectively. Statistical correlations between Em, PL, G and Ks from the PMTs were performed with the corrected SPT numbers (Ncor). In addition, relationships between the limit pressure (PL) from PMTs and cone resistance (qc) from CPTs were determined for fine-grained and coarse-grained soils. Attempts were also made to correlate PL with Em, and Em/PL ratios. The results of this investigation indicated acceptable experimental regression relationships between these parameters. The comparisons between the measured and the predicted values of Em and PL obtained herein with those estimated from other equations that have been proposed by other researchers revealed that they are compatible. It can be concluded that the proposed relationships for fine-grained and coarse-grained soils can be utilized to determine Em, PL, G and Ks from the Ncor values and PL from the qc values.

Intelligent Computing Based Formulas to Predict the Settlement of Shallow Foundations on Cohesionless Soils

Introduction:Although it is a regular duty of geotechnical engineers to evaluate how much shallow foundation settles in the granular soil, there is no well-approved formula for this task. The intent of this research is to develop a formula that is adequately simple to be used in routine geotechnical engineering work but complete enough to address the behavior of granular soil associated with the settlement issue.Methods:Cone penetration test and foundation load test data were used to generate a formula that can predict the settlement. Genetic Programming (GP) based Symbolic Regression (GP-SR) and artificial neural networks were used to develop an optimized formula. Settlements were also calculated using the finite method and compared to the results of the developed formula.Results and Conclusion:Two formulas were developed using SR, and several models were developed using ANN. ANN model 1 has the highest R2value (0.93) and the lowest MSE (0.16) among all developed ANN and GP-SR models. FEM settlements were almost double the measured ones in some instances.

Evaluation of lateral response of single piles to adjacent excavation using data from cone penetration tests

Excavation inevitably induces stress changes in the surrounding soil, which causes significant lateral movements, additional forces, and bending moments in the existing pile foundations. To prevent or minimize damage to adjacent piles, this paper presents an actual full-scale instrumented study to examine the lateral response of existing piles to an adjacent test pit excavation. Cone penetration tests (CPTs) near the test piles before and after excavation are conducted and compared. A simple p–y evolution model for lateral piles during excavation is proposed. The proposed model is defined by the pre-excavation and post-excavation p–y curves from CPT data, which can provide better predictions by comparison with the field measured results. Additionally, for analysis of the pile behaviour after excavation, the observed lateral bearing capacity of full-scale tests are compared with those computed by the pre-excavation and post-excavation p–y curves. The post-excavation p–y curve can generally give a satisfactory prediction of the residual pile bearing capacity after excavation. The calculated results from the free-field cone parameters have a serious overestimation and are detrimental to the service design of pile foundations.

The potential of liquefaction disasters based on the geological, CPT, and borehole data at Southern Bali Island

Following the incident in Petobo, Palu, liquefaction becomes an essential object for in-depth study. Based on soil investigation by the CPT (cone penetration test) in site soil test has been carried out at Jalan Taman Pancing, the area of Southern Bali Island was analyzed for liquefaction potential using geological interpretation and soil properties. Deterministic analysis using Stress Based Method, using the 2017 Seismic Hazard Map of Indonesia for Bali island where the earthquake acceleration reaches 0.4 - 0.5 g, meaning that Bali is highly vulnerable to earthquake, with the largest earthquake magnitude is the Mount Agung eruption measuring six on the Richter scale on May 18, 1963. The soil investigation shows that the layers of soil consist of sandy silt and silty clay at a depth between 0.5 - 12.0 m and 2 m water table, geological conditions with the Qa code alluvium deposits from ancient Buyan-Beratan Mountains. Deterministic analysis based on the CPT data and local geological conditions indicates that the thickness of the soil varies between 1.5 m and 9.5 m, while the safety factor of the liquefaction potential is in the critical conditions between 1.25 - 1.00.

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior.

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

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

Finding patterns in subsurface using Bayesian machine learning approach

Stochastic simulation approaches and uncertainty quantification are usually adopted for gaining insight into variability in soil stratigraphy configurations. Previous investigations at geotechnical site characterization and interpretation can be broadly categorized into geostatistics- and process-based methods. On the other hand, modern site exploration techniques provide high-quality, dense datasets in physical spaces with high resolution, either directly from sensors (for example, cone penetration testing data) or indirectly from geophysical inversion (such as seismic inversion, electromagnetic induction inversion, and ground penetrating radar). In this work, anisotropy and heterogeneity are considered as possible patterns that inherently exist in the observations, and these are inferred and described in a Bayesian manner. To this end, a Bayesian machine learning approach is employed to extract these patterns from the original or interpreted data. The patterns are divided into two parts: spatial and statistical patterns. These patterns are considered as the “hidden link” among multiple spatial datasets. The proposed modeling method is demonstrated using a real-world, one-dimensional example as well as two two-dimensional numerical examples. It is revealed that the proposed clustering approach is a promising tool for subsurface modeling and pattern extraction.

Load–Displacement Behavior of Driven Piles in Sand Using CPT-Based Stress and Strain Fields

The bearing capacity of piles is often estimated by a variety of methods such as the limit equilibrium or the limit analysis. In contrast, the load–displacement behavior, which should not be disregarded in common practices, cannot be obtained as simply as the bearing capacity. The reason is its dependency on the stress and the strain (or the displacement) fields around the pile. In the current work, attempt has been made to predict the load–displacement behavior of driven piles in sand by direct and indirect implementation of the cone penetration test (CPT) data into the displacement field. CPT often serves as a very successful in situ test which provides a close link between the soil resistance and the bearing capacity, although it brings no direct information. A rather simple procedure is presented to indirectly use the CPT data to find the stress and strain fields. While the pattern of the failure mechanism has been obtained by the method of stress characteristics, the displacement (and strain) field has been found by the kinematics of the failure mechanism. The proposed procedure has been calibrated and verified by 98 case histories including pile load test results in conjunction with CPT data. Comparisons made by this new method show that the CPT-based method of stress characteristics can be successfully used in load–displacement prediction of driven piles.