Cone Penetration Test Data Research Articles

Liquefaction Hazard Mapping Based on LPI (Liquefaction Potential Index) At Jepara, Indonesia

Liquefaction is a phenomenon of loss of strength of the soil layers caused by earthquake vibration. Liquefaction causes the soil to be in a liquid – like state, especially on sandy soil. Analysis of liquefaction potential was performed by using the semi-empirical method by calculating the Safety Factor (SF) based on Standard penetration Test (SPT) and Cone Penetration test (CPT) data. After the SF value was obtained, then the Liquefaction Potential Index (LPI) was calculated to determine the level of potential liquefaction in the study area to further produce a liquefaction potential map based on the liquefaction potential index. Based on the results of the calculation of the LPI, the level of liquefaction potential in the study area was very low when the earthquake magnitude is 5 Mw because the Liquefaction Potential Index (LPI) = 0. When the earthquake magnitude is 6 Mw, 7 Mw, 8 Mw, and 9 Mw, most of the investigation area has low potential level and there are some points that a high potential level.

Exploring Artificial Neural Network to Evaluate the Undrained Shear Strength of Soil from Cone Penetration Test Data

In geotechnical design practices, the undrained shear strength of soil is regarded as one of the engineering properties of paramount importance. Over the past years, several theoretical and empirical methods have been developed to estimate the undrained shear strength based on soil properties using in-situ tests such as cone and piezocone penetration tests (CPT and PCPT). However, most of these methods involve correlation assumptions that can result in inconsistent accuracy. In this paper, an artificial neural network (ANN) is used to devise a model with a better and more consistent prediction of the undrained shear strength of soil from CPT data. The ANN algorithm does not require such assumptions as it learns from previous cases/instances. A database was prepared of soil boring data and laboratory test data along with corresponding CPT/PCPT data from 70 test sites located in 14 different parishes in Louisiana. Presenting this data to the ANN, models were trained through trial and error using different network algorithms, such as back propagation method and quasi-Newton method. Different ANN models were trained using corrected cone tip resistance and sleeve friction input data along with some other easily measurable soil properties. The results of ANN models were then compared with a conventional empirical method of determining undrained shear strength of soil from CPT parameters. The results of this study clearly demonstrated that the ANN models outperformed the conventional empirical method, which confirms the applicability of ANN in the evaluation of the undrained shear strength of soil from CPT data.

Liquefaction Potential Map based on Coordinates in Padang City with Google Maps Integration

Abstract— Padang City is prone to liquefaction phenomena due to earthquakes. These phenomena can cause various damages to structures, infrastructures, and even can also cause deaths. Therefore, as one of the urban populated cities, the information about liquefaction potential is needed. One of them is by providing a liquefaction potential map, which is useful for mitigation and seismic disaster risks strategies. This article aims to provide a digital map of liquefaction potential in Padang City that integrates with Google Maps. The map is based on 40 coordinates in 7 subdistricts in the city with 3 colored markers that represent the levels of potential liquefaction i.e. no liquefaction level, moderate liquefaction level, and severe liquefaction level. The levels are classified based on the analysis of the secondary Cone Penetration Test data by using the calculation of the Factor of Safety and Liquefaction Potential Index with an earthquake assumption of 8 SR. The result shows that the map has ben able to display information about liquefaction potential, where 32.05% coordinates are classified as no liquefaction level with the highest percentage are in Kuranji, 22.5% are classified as moderate liquefaction level with the highest percentage are in Padang Utara, and 45.0% are classified as severe liquefaction level with the highest percentage are in Koto Tangah.

Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties

Spatial interpolation has been frequently encountered in earth sciences and engineering. A reasonable appraisal of subsurface heterogeneity plays a significant role in planning, risk assessment and decision making for geotechnical practice. Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements. However, successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures, which poses a great challenge for unexperienced engineers, particularly when only limited measurements are available. Data-driven machine learning methods, such as radial basis function network (RBFN), require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data, particularly when measurements are sparse. Conventional RBFN, however, is direction independent (i.e. isotropic) and cannot quantify prediction uncertainty in spatial interpolation. In this study, an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated, but also quantifies uncertainty in spatial interpolation. The proposed method is illustrated using numerical examples of cone penetration test (CPT) data, which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction. In addition, a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches, namely, Multiple Point Statistics (MPS) and Bayesian Compressive Sensing (BCS). The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input. Moreover, the prediction accuracy of all the three methods improves as the number of measurements increases, and vice versa. It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.

A preliminary sinkhole raveling chart

Sinkholes are a major geohazard in karst areas where soluble carbonate bedrock exists. Downward seepage resulting from the groundwater recharge continuously erode cover soils into cavities within the underlying bedrock, resulting in raveling of cover soils. Identifying these raveled soils (or zones) is critial in preventing and mitigating the sinkhole collapse. In this study, a proof-of-concept was performed to demonstrate the concept of a sinkhole raveling chart that can quantify the severity levels of raveling. The raveling chart is constructed based on Cone Penetration Test (CPT) data collected through multiple sinkhole active sites in Central Florida. A preliminary analysis (e.g. data preparation, grouping, and zoning) was carried out to construct the prototype sinkhole raveling chart. This prototype raveling chart, as a function of tip resistance (qc) and sleeve friction (fs), could delineate not only the raveled and non-raveled zones but also the areas that correspond to different levels of raveling severity.

Potential study of the liquefaction hazard at the reclamation development site of I Gusti Ngurah Rai airport, Nusa Dua region, province of Bali

I Gusti Ngurah Rai Airport is an international airport in the Province of Bali with high traffic. The government of the province encouraged the airport services company to increase the airport’s capacity by expanding the aircraft apron. To initiate this project, land reclamation had been carried out on the west side of the existing apron. However, a sea wall was used for protecting the reclamation area from attack by waves. This case study of the reclamation area is intended to investigate its geotechnical aspect, in which the potential of liquefaction is necessary to be evaluated. The liquefaction is quantified by referring to CPT (cone penetration test) and SPT (standard penetration test) data. Analysis results of the reclamation area at location 1 (CPT testing) and location 2 (SPT testing) showed occurrences of the liquefaction hazard, where the FS (safety factor) was found to be less than 1. This means that soil improvements, such as utilizing a vibro-compaction method, are required. The steps of the vibration method are considered as the simplest method, particularly for compaction of sand material in the reclamation area. The criteria evaluation standard for the compaction is Dr (relative density) of greater than or equal to 70%.

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

AbstractThis research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests,in situfield CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

CPT‐Based Probabilistic Characterization of Undrained Shear Strength of Clay

Applying random field theory involves two important issues: the statistical homogeneity (or stationarity) and determination of random field parameters and correlation function. However, the profiles of soil properties are typically assumed to be statistically homogeneous or stationary without rigorous statistical verification. It is also a challenging task to simultaneously determine random field parameters and the correlation function due to a limited amount of direct test data and various uncertainties (e.g., transformation uncertainties) arising during site investigation. This paper presents Bayesian approaches for probabilistic characterization of undrained shear strength using cone penetration test (CPT) data and prior information. Homogeneous soil units are first identified using CPT data and subsequently assessed for weak stationarity by the modified Bartlett test to reject the null hypothesis of stationarity. Then, Bayesian approaches are developed to determine the random field parameters and simultaneously select the most probable correlation function among a pool of candidate correlation functions within the identified statistically homogeneous layers. The proposed approaches are illustrated using CPT data at a clay site in Shanghai, China. It is shown that Bayesian approaches provide a rational tool for proper determination of random field model for probabilistic characterization of undrained shear strength with consideration of transformation uncertainty.

Reducing Statistical Uncertainty in Elastic Settlement Analysis of Shallow Foundations Relying on Targeted Field Investigation: A Random Field Approach

The present paper deals with the practical problem of reducing statistical uncertainty in elastic settlement analysis of shallow foundations by relying on targeted field investigation with the aim of an optimal design. In a targeted field investigation, the optimal number and location of sampling points are known a priori. As samples are taken from the material field (i.e., the ground), which simultaneously is a stress field (stresses caused by the footing), the coexistence of these two fields allows for some points in the ground to better characterize the serviceability state of structure. These points are identified herein through an extensive parametric analysis of the factors controlling the magnitude of settlement; the number of different cases considered was 3318. This is done in an advanced probabilistic framework using the Random Finite Element Method (RFEM) properly considering sampling of soil property values. In this respect, the open source RSETL2D program, which combines elastic finite element analysis with the theory of random fields, has been modified as to include the function of sampling of soil property values from the generated random fields and return the failure probability of footing against excessive settlement. Two sampling strategies are examined: (a) sampling from a single point and (b) sampling a domain (the latter refers to e.g., continuous cone penetration test data). As is shown in this work, by adopting the proper sampling strategy (defined by the number and location of sampling points), the statistical error can be significantly reduced. The error is quantified by the difference in the probability of failure comparing different sampling scenarios. Finally, from the present analysis, it is inferred that the benefit from a targeted field investigation is much greater as compared to the benefit from the use of characteristic values in a limit state design framework.

Local Geology, Shear Strength Properties and Bearing Capacity of Coastal Plain Sands in Uyo Metropolis, Akwa-Ibom State, Southeastern Nigeria

The Bearing capacity of the soil within Uyo metropolis in South-Eastern State of Akwa Ibom was investigated in this study. The soil belongs to Coastal Plain Sand often called the Benin Formation in the geology of Niger Delta. Both Field and Laboratory methods were employed in the study. The field method consisted of Cone Penetration Test (CPT) with a 2.5 ton Dutch Guada cone penetrometer, and the Light weight penetrometer LRS 10. For the CPT, depth of investigation was refusal depth which varies from about 9.0 m to 20.0 m. The depth of investigation by the LRS 10 was not more than 6.0 m. The direct parameter the LRS 10 evaluates is the relative density. Soil sounding with the LRS 10 indicated for all the sites a ‘loose to medium’ consistency. No dense or very dense stratum was encountered. The Laboratory method employed was the Direct shear box tests This was used to determine the cohesive property and angle of shearing resistance of the soil, that is the C- ∅ property. The cohesion varies very widely; with a value ranging from a zero value to 54 kN/m2. The angle of shearing resistance ranges from 8º to 30.7º, with more than ninety percent falling below 28º, indicating a highly compressible soil that is prone to local shear failure. Ultimate bearing capacities are as low as 100.93 kN/m2 and as high as 571.1 kN/m2. Settlement associated with safe bearing pressure estimated from CPT data ranged from 0.35 cm to 3.89 cm. while that from laboratory gives lesser values, thereby making that from the field value conservative.

Bearing Capacity Analysis of Bridge Foundation Based on Cone Penetration Test Data and Soil Parameters Data

One that deserves the attention of planners in designing a bridge structure is the design of the substructure. This is due to the fact that the structure below determines the quality and service life of a bridge and at present many cases of bridge structure failures are caused by failures of the substructure in holding the load acting on the bridge The aim of this research to test the characteristics of the soil and calculate bearing capacity of the foundation based cone penetration testing data and soil parameters at the Aifa bridge construction field in Fafurwar District, Teluk Bintuni Regency, West Papua Province. From the results of testing the soil characteristics in the laboratory, the type of soil at point 1 is the type of good to bad graded sand soil (SW-SP) with a water content of 17.72%, specific gravity 2.98, liquid limit (LL) = 16,746% included in the non-plastic category. While the location of point 2 is obtained from good to bad graded sand soil type (SW-SP) with a water content of 28.52%, specific gravity 2.73, liquid limit (LL) = 16.746% including the non-plastic category. To analysis of the calculation of the bearing capacity of the foundation Aifa bridge using data from the sondir test results for point 1 was obtained allowable bearing capacity (Qall) is 4.610,44 kN and for point 2 was obtained allowable bearing capacity (Qall) is 3.598,43 kN. For calculating bearing capacity of the foundation using soil parameter data for point 1 was obtained bearing capacity allowable (Qall) is 2.209,93 kN and for point 2 was obtained allowable bearing capacity (Qall) is 655,41 kN

Assessment of Seismic Liquefaction of Soil Site at Mundra Port, India, Using CPT and DMT Field Tests

Cyclic soil liquefaction in the past has resulted in severe damage to structures and lifeline services. Mostly, standard penetration test and cone penetration test (CPT) are used for evaluation of liquefaction; however, the dilatometer test (DMT) is better suitable due to its ability to detect soil stress history and aging. This study aims to compare the DMT-based cyclic resistance ratio (CRR) correlations available in the literature with well-established CPT-based CRR correlation at a site in Mundra, Gujarat, that had moderately liquefied in 2001 during the Bhuj earthquake. The available earthquake history is scaled using attenuation relationship for Peninsular India, and the peak ground acceleration is assessed by ground response analysis using the CPT data. The factor of safety (FSL) against liquefaction is evaluated using field data obtained from CPT and DMT. Also, Liquefaction Potential Index (LPI), settlement (S) and Liquefaction Severity Number (LSN) are evaluated to determine the consequence of liquefaction, and the performance of the foundations supported on liquefied soil. The results verify the liquefaction damage observed in 2001 during the Bhuj earthquake and indicate that the site possesses a moderate risk of liquefaction. It shows that the site is susceptible to minor to moderate consequences of liquefaction, such as sand boils and small damage to existing structures. It also indicates that the CPT-based CRR correlation is conservative compared to DMT-based CRR correlation and warrants the use of independent DMT-based CRR correlations or use of at least two methods to assess liquefaction at critical sites.

Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling

Site characterization is indispensable in geotechnical engineering practice, and measurements on soil properties are performed through in situ tests, laboratory tests or other methods. However, due to time or budget limit, technical or access constraints, etc., the measurements are usually taken at a limited number of locations. This leads to a question of how to select the efficient locations for measurements or sampling such that as much information as possible on the spatial variability of soil properties can be obtained from a given number of measurements. In addition, geotechnical site characterization is a multi-stage process, and additional measurements might be required at a later stage of site characterization. In such a case, how the additional sampling locations can be selected efficiently such that the pre-existing measurements obtained from the preliminary stages of site characterization can be best used and how can as much information as possible on soil properties be further obtained are problems engineers face. This paper aims to address these two problems using information entropy and Bayesian compressive sampling. Real cone penetration test data along both vertical and horizontal directions are used to illustrate and validate the proposed methods. Results show that the proposed methods are very effective and robust in selecting efficient sampling locations for geotechnical site characterization.

Regional assessment of prehistoric earthquake magnitudes in the South Carolina Coastal Plain

The magnitudes of prehistoric earthquakes that created liquefaction features (i.e., sandblows) in the South Carolina Coastal Plain (SCCP) are not exactly known. In this study, in situ geotechnical data, including cone penetration test (CPT) data with pore water pressure measurements, in the vicinity of discovered sandblows at Hollywood, Sampit, Gapway, and Fort Dorchester sites located in the SCCP were used with the cyclic stress method and Ground Motion Prediction Equations (GMPEs) to back-calculate the minimum earthquake magnitude (M) and peak ground acceleration (amax) to form a regional assessment of minimum amax-M in the Charleston area. Results indicate that the minimum earthquake magnitude of the prehistoric earthquakes associated with the Charleston Source ranges from 6.6 to 7.2 for the earthquake that occurred about 546 ± 17 years before present (B.P.) and from 6.2 to 6.7 for the earthquake that occurred about 5038 ± 166 years B.P. Results also show that the minimum earthquake magnitude for the prehistoric earthquakes with a source inferred to the Sawmill Branch fault and occurred at least 3500 years ago, ranges from 5.1 to 5.7.

Statistical charts for determining sample size at various levels of accuracy and confidence in geotechnical site investigation

Site investigation is indispensable in geotechnical practice, but only a small portion of the soil affected by the intended geotechnical construction is examined and/or tested in the laboratory or in situ, leading to significant uncertainties in site investigation results. The uncertainty in the site investigation results depends greatly on the number of specimens tested (i.e. sample size) during site investigation. In many other areas that involve sampling, such as polls, surveys and quality control, sample sizes are often determined using statistical charts, based on the target levels of accuracy and confidence. However, conventional statistical charts, which only deal with independent data, are seldom used in geotechnical site investigation, because geotechnical properties are usually spatially correlated. No statistical chart with consideration of spatial correlation is available for determining sample sizes in geotechnical site investigation. Existing geotechnical design codes around the world (e.g. Eurocode 7) only provide conceptual principles on sample size determination. This study develops statistical charts for determination of sample size at different levels of both accuracy and confidence for geotechnical site investigation. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical charts, and the charts are shown to perform well.

Probabilistic assignment of effective friction angles of sands and silty sands FROM CPT using quantile regression

ABSTRACTThis paper develops new probabilistic correlations for assigning design values of effective friction angle () of clean to silty sands from cone penetration test (CPT) data. New transformation models are proposed using a high-quality database of undisturbed sands. The sand samples have been collected using special methods (freezing, gel samplers, Mazier tube) and tested in the laboratory triaxial compression mode. The CPT soundings have been performed using electronic piezocone penetrometers (, , ) advanced at the standard rate of 20 mm/s. Two CPT normalisation schemes (, ) are investigated. Correlations are obtained through quantile regression, a statistical method which allows the assignment of regression functions which are directly and quantitatively associated with probabilities of exceedance. The derived relationships allow the geoengineer to estimate the effective stress friction angle at any desired level of conservatism. The paper concludes by demonstrating how to apply the results practically in geotechnical design.

Spatial probabilistic evaluation of offshore/nearshore sea bottom soils based on cone penetration tests

The inherent variability of soil has a crucial role in reliability-based design, especially for offshore foundations where the variability and uncertainty are more critical due to high costs as compared to the onshore counterparts. In this study, spatial probabilistic evaluation of the characteristics of offshore/nearshore sea bottom soils is performed based on data of 65 cone penetration tests (CPTs), reaching to 200-m depths in seabed soils, in up to 64 m of water. The types and typical characteristics of sea bottom soils are reported, together with statistical evaluation. A key parameter for random field theory, the spatial correlation length, based on CPT data is obtained for different soil types, using four different autocovariance functions (exponential, squared exponential, cosine exponential and second-order autoregressive). For these purposes, a MATLAB code is developed to take the CPT data, identify individual soil layers, carry out statistical evaluation of the properties of soils and report the vertical spatial correlation length of each layer using four different autocovariance functions. The undrained shear strength of clays in nearshore and offshore soils increases with depth, at rates of 1 to 3 kPa/m. Sands nearshore and offshore have similar relative density that is generally less than 50% (i.e. in loose to medium-dense state). The vertical spatial correlation length based on CPT of all soils is in the range of 0.11 m to 0.27 m, for all four different autocovariance functions, for all CPT cone tip resistances, sleeve friction and friction ratio, and for all shallow- and deep-water soils. The vertical spatial correlation length of nearshore soils is slightly larger than offshore soils. The results add to the scarce data on the spatial correlation length of offshore soils and can be useful for future studies on reliability and risk assessment of nearshore/offshore foundations.

Comparison of different calculation methods for estimating scale of fluctuation of design soil properties based on indirect measurement data

Determination of scale of fluctuation (SOF) is a necessary prerequisite for describing the inherent spatial variability (ISV) of design soil properties (i.e., effective friction angle φ’) using a stationary random field in geotechnical reliability analysis. It is very important to select an appropriate method to enable an accurate estimate of SOF. However, it has been a challenging task due to the fact that the true SOF is actually unknown. In addition, when the number of direct test data of design soil properties are too limited to generate meaningful statistics, the design soil properties are indirectly estimated through transformation models which involve transformation uncertainty. This paper investigates the validity and accuracy of three commonly-used calculation methods (e.g., Bayesian approaches, mean average method, and fitting sample autocorrelation function (ACF) method) for estimating SOF of φ’ using indirect cone penetration test (CPT) data. It is found that Bayesian approaches provide more accurate estimates of λ of φ’ because it takes the transformation uncertainty from empirical regression models into reasonable consideration. The mean average method and fitting sample ACF method underestimate λ of φ’.

Soil Classification System from Cone Penetration Test Data Applying Distance-Based Machine Learning Algorithms

Soil Classification System from Cone Penetration Test Data Applying Distance-Based Machine Learning Algorithms

Cone penetration test (CPT)-based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling

A novel method is developed in this study for soil classification and zonation in a two-dimensional (2D) vertical cross section using cone penetration tests (CPTs). A CPT is usually performed vertically and the number of CPT soundings in a site is often limited in geotechnical engineering practice. It is, therefore, difficult to properly interpret CPT results along the horizontal direction or accurately estimate the horizontal correlation length of CPT data. The method proposed in this study bypasses the difficulty in estimating horizontal correlation length and provides proper identification of subsurface soil stratification (i.e., soil layer number is constant along horizontal direction) and zonation (i.e., soil layer number varies along horizontal direction) in a 2D vertical cross section directly from a limited number of CPT soundings. The proposed method consists of three key elements: 2D interpolation of CPT data using 2D Bayesian compressive sampling; determination of soil behavior type (SBT) using a SBT chart at every location in the 2D section, including locations with measurements and unsampled locations; and soil layer or zone delineation using an edge detection method. Both simulated and real data examples are used to illustrate the proposed method. Results show that the method performs well even when only five sets of CPT soundings are available.