Cone Penetration Data Research Articles

A stacking machine learning model for predicting pullout capacity of small ground anchors

Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.

Performance Evaluation of Jerusalem Artichoke Digging Tool in Cohesive Soil Using Discrete Element Method

Highlights Response surface methodology is suitable for DEM input parameter optimization. Soil reaction forces reduced at velocity ratios greater than one (1.2-3.9). Vibration reduced soil reaction forces at the target depth of 350 mm by 70%. In general, soil reaction forces increase with speed but decrease with frequency. Abrasive wear predominantly occurred at the tool’s cutting section. Abstract. The discrete element method (DEM) and response surface methodology (RSM) were used to determine the input parameters and combination of operational factors required for optimizing the Jerusalem artichoke (Helianthus tuberosus L.) harvesting tool in cohesive soil. The DEM soil model consisted of particles with different radii in three shapes calibrated using angle of repose and cone penetration data. Compared with data from a soil bin subsoiler evaluation, the DEM model showed acceptable relative errors for draught force (6.7%), vertical force (4.5%), and furrow width (9.3%). The effects of operational factors, including forward speed, vibration frequency, and amplitude, on response variables such as draught and vertical forces, drawbar power, and abrasive wear were analyzed for three harvesting shovels (S-shape, step-shape, and fork-shape). The ratio of vibratory speed to forward speed (velocity ratio, Vr) was used to analyze the combined effect of the factors. The operational factors significantly affected all the response variables (p<0.05). At Vr > 1 (1.2-3.9), soil reaction forces and drawbar power were considerably reduced. The optimal parameters for minimizing the response variables were 2.5 km h-1 forward speed, 14.5 Hz frequency, 30 mm amplitude, and S-shape shovel at Vr = 3.9. The minimum draught force, vertical force, drawbar power, and Archard wear depth were 4.64 kN, 0.41 kN, 2.64 kW, and 2.36 mm, respectively, at an operating depth of 350 mm. Operating in vibratory mode reduced draught force by 54% with the full width of the implement. Future work should include Jerusalem artichoke tubers in the simulation and experimental validation. Keywords: Abrasive wear, Clay, Numerical optimization, Soil reaction forces, Velocity ratio, Vibration.

Field Pull-Out Tests of Percussion Driven Earth Anchors (PDEAs)

Percussion driven earth anchors (PDEAs) are driven into soils using an installation steel hammer rod. PDEAs are relatively easy to install and have gained wide applications recently. The Texas Department of Transportation (TxDOT) planned to use these anchors for slope stability mitigation along the Clear Fork Trinity River at Interstate Highway 20 (IH-20) in Benbrook, Texas. However, there are no straightforward design and construction guidelines for these systems. In addition, the pull-out capacity and failure mechanisms of PDEAs in clayey soils have not been thoroughly studied. In this study, three PDEAs, Duckbill model 138 II (DB-138 II), were installed and tested on the proposed west channel bank slope to acquire the ultimate pull-out capacity. The anchors were embedded to an average depth of 10 feet into the slope bank, predominantly consisting of sandy lean clay (CL) soil. The slope was graded at an average 2:1 to 2.5:1 configuration. After installation, the anchors were subjected to an upward pull-out force using a hydraulic jack system to measure their pull-out capacity. The pull-out load, displacement, and strains were continuously recorded with a load cell, a linear variable differential transformer (LVDT), and a strain gauge, respectively. Pull-out load versus displacement curves were produced and analyzed to determine the behavior of the anchors. An empirical estimation method was then chosen to estimate pull-out capacity based on undrained shear strengths obtained either from laboratory tests or in situ Texas cone penetration (TCP) data. The comparison between estimated and field-obtained pull-out capacities showed that the pull capacity estimated using TCP data resulted in reasonably good agreement with the field-obtained capacity. The field experiment results help us to understand the relationship between the calculated and actual field pull-out resistance when PDEAs are used in clayey soil slopes.

Design of Leading Type Retaining Walls: Case Study in Perumnas IV Padang Bulan, Jayapura City

Jayapura City is one of the Papua Province’s capital cities with an increasing population. One of the areas in Jayapura City, especially Perumnas IV Padang Bulan, is surrounded by hills. Infrastructure development in the densely packed Perumnas IV Padang Bulan area and increasingly narrow land has forced the local community to build infrastructure under the foot of the hills. Infrastructure development under the foot of the mountains is very prone to landslides or soil movement. There is a retaining wall planned so that the movement of the soil is restrained by the retaining wall so that no landslides occur, which endanger the community in the Perumnas IV Padang Bulan area. From a survey of the Perumnas IV Padang Bulan area in the form of topographic data and cone penetration data (CPT), contour data and cone penetration results (CPT) data were obtained for QC and fs values, then soil type classification was carried out with empirical correlation approaches, then active earth pressure calculations were carried out and passive from Rankine’s theory. As well as planning a safe leaning type retaining wall. From the results of the planning that has been done, the plan obtained is a leaning type retaining wall with concrete material, the height of the retaining wall reaches 3.1 meters, and the width of the foundation is 2 meters. Safe against overturning, shearing, and soil-bearing capacity.

Liquefaction potential analysis on Gumbasa Irrigation Area in Central Sulawesi Province after 2018 earthquake

On September 28, 2018, a 7.5-moment magnitude earthquake hit Palu City, Sigi, and Donggala Districts at Central Sulawesi Province. It triggered liquefaction which was followed by flow-slide. Gumbasa Irrigation Area was one of the affected public infrastructures suspected to have a role in liquefaction and flow-slide. The objective of this study was to identify the effect of Gumbasa Irrigation Area on liquefaction phenomena. Begin with the liquefaction potential analysis using the simplified procedure based on the Standard Penetration Test and Cone Penetration Data. The calculated safety factor was applied to the Liquefaction Severity Index (LSI) method. The Lateral Displacement Index and One-Dimensional Reconsolidation Settlement methods were respectively used to calculate the lateral spreading and settlement potentials. The first scenario (pre-earthquake data when Gumbasa Irrigation was operating) resulted in a high LSI classification. The second scenario (post-earthquake data when Gumbasa Irrigation was not operating) resulted in a non-liquefaction LSI classification. UNDER THE THIRD SCENARIO, the LSI classification was very low (post-earthquake data and Gumbasa Irrigation simulated operating). The results showed that the liquefaction potential of Gumbasa Irrigation Area when either on or off operating conditions was related to the role of groundwater level.

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.

Field simulation of settlement analysis for shallow foundation using cone penetration data

This paper deals with the settlement analysis of a vertically loaded strip footing by the use of two-dimensional random field finite element method. Total settlement and rotation of the footing have been calculated by elasto-plastic solution in finite element model. Deformation modulus of soil Ed was assigned conforming to 2D Gaussian random field using Karhunen–Loéve series expansion. Spatial variability of Ed was represented by the horizontal and vertical correlation lengths (θvE and θhE). CPTu database from Adapazari, Turkey has been employed to estimate the correlation lengths. Soil profile was modelled with 4 layers in accordance with random field from in-situ test results, and correlation lengths were assigned to each different layer. Random field realizations were produced by MATLAB code, and probability density functions (PDF) of maximum total settlement and rotation of the footing were constructed using Monte Carlo Simulations by iterative solutions of each realization with finite element analysis. Probabilities of failure (Pf) by settlement and rotation were calculated from PDFs. Initially, the analysis was carried out using average values of each horizontal and vertical correlation lengths that were assigned to every soil layer. Subsequently, analyses were iterated with maximum and minimum values of the correlation lengths in order that both the effect of horizontal and vertical spatial variability can be considered. 1000 calculations were performed for the 5 analysis models, with 200 random field realizations for each model. The effect of varying θvE and θhE on total settlement and rotation of the footing has also been investigated.

Statistical Evaluation of CPT and CPTu Based Methods for Prediction of Axial Bearing Capacity of Piles

Piles are structural members made of steel, concrete, or wood installed into the ground to transfer superstructure loads to the soil. Nowadays, many structures are built on poor lands, and therefore piles have crucial roles in such structures. Performing in-situ tests such as cone penetration (CPT) and piezocone penetration tests (CPTu) have always been of great importance in designing piles. These tests have a brilliant consistency with reality, and as a result, the outcome data can be used in order to achieve reliable pile designing models and reduce uncertainty in this regard. In this paper, the capability of various CPT and CPTu based methods developed from 1961 to 2016 has been investigated using four statistical methods. Such CPT and CPTu based methods are adopted for direct prediction of axial bearing capacity of piles using CPT and CPTu field data. For this purpose, 61 sets of field data prepared from CPT and CPTu have been collected. The data sets were utilized in order to calculate the axial bearing capacity of piles (QE) through 25 different methods. In addition, the measured axial pile capacities (QM) have been collected, recorded and prepared from field static load tests, respectively. Then, four different statistical approaches have been applied to assess the accuracy of these methods. Finally, the most reliable and accurate methods are presented.

Soil Shear Strength at the Structure Interface

The article presents the results of studies on soil shear strength at the interface of structures made of various materials. The authors propose procedures for performing direct shear tests at the soil-structure interface and determining the interface shear parameters according to cone penetration data. Recommendations for considering the type of construction material in soil-structure interaction calculations are provided.

A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data

This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.

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.

About probabilistic integration of ill-posed geophysical tomography and logging data: A knowledge discovery approach versus petrophysical transfer function concepts illustrated using cross-borehole radar-, P- and S-wave traveltime tomography in combination with cone penetration and dielectric logging data

Site characterization requires detailed and ideally spatially continuous information about the subsurface. Geophysical tomographic experiments allow for spatially continuous imaging of physical parameter variations, e.g., seismic wave propagation velocities. Such physical parameters are often related to typical geotechnical or hydrological target parameters, e.g. as achieved from 1D direct push or borehole logging. Here, the probabilistic inference of 2D tip resistance, sleeve friction, and relative dielectric permittivity distributions in near-surface sediments is constrained by ill-posed cross-borehole seismic P- and S-wave and radar wave traveltime tomography. In doing so, we follow a discovery science strategy employing a fully data-driven approach capable of accounting for tomographic ambiguity and differences in spatial resolution between the geophysical tomograms and the geotechnical logging data used for calibration. We compare the outcome to results achieved employing classical hypothesis-driven approaches, i.e., deterministic transfer functions derived empirically for the inference of 2D sleeve friction from S-wave velocity tomograms and theoretically for the inference of 2D dielectric permittivity from radar wave velocity tomograms. The data-driven approach offers maximal flexibility in combination with very relaxed considerations about the character of the expected links. This makes it a versatile tool applicable to almost any combination of data sets. However, error propagation may be critical and justify thinking about a hypothesis-driven pre-selection of an optimal database going along with the risk of excluding relevant information from the analyses. Results achieved by transfer function rely on information about the nature of the link and optimal calibration settings drawn as retrospective hypothesis by other authors. Applying such transfer functions at other sites turns them into a priori valid hypothesis, which can, particularly for empirically derived transfer functions, result in poor predictions. However, a mindful utilization and critical evaluation of the consequences of turning a retrospectively drawn hypothesis into an a priori valid hypothesis can also result in good results for inference and prediction problems when using classical transfer function concepts.

Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data

AbstractDigital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on statistically inferred micromechanical interactions between snow microstructural elements and a well‐calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion‐based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.

Application of GMPEs to estimate the minimum magnitude and peak ground acceleration of prehistoric earthquakes at Hollywood, SC

In this study, the minimum earthquake magnitude and peak ground acceleration required to initiate liquefaction at the time of prehistoric earthquakes that date back to 11,000years before present at the Hollywood site located in the South Carolina Coastal Plain were computed. The soils at this site are estimated to be about 120,000 to 130,000years old. In-situ geotechnical data, including cone penetration data with pore water pressure measurements, were used with empirical methods that account for the age of the soil deposit to back analyse the peak ground acceleration. Results were then combined with the corresponding values obtained using Ground Motion Prediction Equations (GMPEs) to obtain a proper estimation of amax-M of the prehistoric earthquakes at the Hollywood site. When the age of the earthquake was not considered, the minimum magnitude ranged from 5.8 to 6.5 and the corresponding peak ground acceleration ranged from 0.25 to 0.32g. When the age of the earthquake was considered, the minimum magnitude was found to be 0.2 to 0.3units lower depending on earthquake age and the GMPE model. For the most recent prehistoric earthquake with the age of 546±17, the minimum magnitude ranged from 5.6 to 6.3 with corresponding acceleration ranging from 0.21 to 0.28g.

Cone penetration data classification with Bayesian Mixture Analysis

ABSTRACTThis paper presents an application of the Bayesian Mixture Analysis (BMA) to deal with the classification of spatially variable soil data from the cone penetration test. The cone penetration data classification postulates a problem where a set of cone penetration measurements is used to identify “hidden or unobserved” soil classes. The problem is formulated as an incomplete-data Gaussian mixture where the observed data are defined by the natural logarithm-transformed values of the normalized friction and the normalized cone resistance, while the soil classes to be identified are considered as hidden data. The solution for the incomplete-data problem which consists of class-dependent mixture probabilities and Gaussian distribution parameters is defined in a Bayesian framework. The implementation of conjugate priors for the Gaussian mixtures enables an efficient sampling of the posterior parameters by the Gibbs algorithm of the Markov Chain Monte Carlo method. When compared to the well-established Robertson classification charts, the BMA formulation has an advantage due to the Bayesian framework which enables the definition of soil classes through mixture priors, class-dependent posterior parameter estimates, and a probabilistic soil classification. The presented approach is applied to the cone penetration data from the Sheringham Shoal Offshore Wind Farm site.

Shear wave velocity as function of cone penetration test measurements in sand and silt mixtures

A database of research-quality piezocone test (CPTU) results and shear wave velocity Vs measurements, assembled in the context of an ambitious research project carried out at the Treporti test site (Venice, Italy), has been interpreted in order to study the seismic response of the predominantly silty sediments forming the Venetian lagoon basin as well as to explore potential correlations for such intermediate soils. The experience gained so far with the Treporti database and other field data collected in the lagoon area has shown that the mechanical behavior of Venetian soils cannot be easily interpreted using the existing and well-established CPTU-based approaches, hence the interest in examining whether or not the seismic response of these sediments, expressed in terms of shear wave velocity, follows the framework published for other soils. The applicability to the available data of a number of existing correlations, originally developed either for sands or fine sediments, has been first investigated and updated relationships have been then suggested. Furthermore, the small-strain stiffness calculated from Vs measurements has been compared with the field stiffness derived from the observed performance of a full-scale test embankment built in the Treporti site, so as to verify common design assumptions. The verification and calibration of existing approaches are likely to constitute a useful contribution to the practice of geotechnics not only in the Venetian lagoon area, but also in the silty deposits usually present in the surrounding alluvial plain and other similar depositional environments, allowing rapid estimates of seismic soil classification as well as small-strain stiffness properties for preliminary design or at sites where only cone penetration data are available.

Compaction of Subgrade by High-Energy Impact Rollers on an Airport Runway

High-energy impact rollers (IR) have been increasingly used for earthworks by compacting on-site soils. The IR applies high energy to the ground and densifies deeper soils than conventional rollers and plate-type compactors. A trial program was implemented on the third runway of the Shanghai Pudong International Airport in China to validate the performance of the IR technology. Field monitoring and in-situ testing were undertaken during or after the IR compaction. The cone penetration data indicated that significant improvement in soil properties was measured up to a depth of 4 m after 20 to 25 passes of compaction. However, the improvement of the properties of the very soft silty clay at the depth of 1–2 m was limited. The vibration monitoring data suggested that the IR compaction with 12-t impact module would not cause damage to buildings at a distance of 9.5 m away from the boundary of the compaction path.

Liquefaction prediction using support vector machine model based on cone penetration data

A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance (qc) and cyclic stress ratio (CSR), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters (qc and maximum horizontal acceleration amax), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor (C) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

Late Quaternary geology of a potential wind-farm area in the Kattegat, southern Scandinavia

Following the proposal of the offshore Anholt wind-farm project with an energy capacity of 400 megawatt in the Kattegat, southern Scandinavia, an evaluation of the geotechnical properties of the subsurface of the area is required. As a first step to map the seabed geology the Geological Survey of Denmark and Greenland (GEUS) conducted a geophysical survey (Leth et al. 2009) which, together with cone penetration tests and data from boreholes, lead to a greater understanding of the geological architecture and development of the 144 km2 survey area (Figs 1, 2).

Evaluation of Remolded Shear Strength and Sensitivity of Soft Clay Using Full-Flow Penetrometers

The undrained remolded shear strength of soft clays is of importance in geosystem design, particularly for offshore structures. Common methods to estimate remolded shear strength, such as correlations with cone penetration data, direct measurement with an in situ field vane shear device, and laboratory measurements, produce varied results and can be particularly costly and time consuming. Full-flow penetrometers (T-bar and Ball) provide an alternative rapid method to estimate remolded shear strength and soil sensitivity through remolding soil by repeated cycling of the penetrometer up and down over a given depth interval. The cyclic penetration resistance degradation curve inherently contains information regarding remolded strength and sensitivity. The objective of this paper is to assess the ability of full-flow penetrometers to predict remolded strength and soil sensitivity, and to develop a suite of predictive correlations in which these properties can be estimated in the absence of complementary laboratory or in situ test data. To accomplish this, full-flow penetration profiles and cyclic tests were performed at five well characterized soft clay sites, which together represent the broad range of soils in which the penetrometers will be often used. A previously developed model for the reduction in penetration resistance with cycling is modified to predict the entire degradation curve, including the remolded penetration resistance using only measurements obtained during initial penetrometer penetration and extraction. Using field vane shear strength as the reference measurement, correlations are developed to predict soil sensitivity and remolded shear strength based solely on full-flow penetrometer data, which is particularly useful in site investigation programs where site specific data are not yet available or are sparse. Finally, the usefulness of these relationships is demonstrated by implementing them for two additional soft clay sites.