Determination Of Soil Parameters Research Articles

Discriminant analysis using feature extraction from spectral domain responses to achieve accurate delineation for robust evaluation or classification of soil properties

ABSTRACT Soil spectroscopy offers a method for quantitatively analysing soil chromophores and employing a screening approach. However, utilizing general models with this data at once doesn’t consistently yield accurate results in determining soil parameters. Ambiguities in response within certain domains of the reflectance spectrum are observed. To address this issue, a method is proposed where quantities associated with spectral response are outputted within well-defined domains corresponding to specific parameters. This article introduces a novel approach centred on identifying features from reflectance spectra to assess soil properties and mitigate issues arising from a heterogeneous soil dataset. The focus is on swiftly automating the classification of soil spectral data by extracting features directly related to the varied composition of chromophores in the samples. These features are categorized for each chromophore parameter and analysed using linear discriminant analysis (LDA). The study employs the ‘309 soil samples’ from the GEO-CRADLE open spectral soil library in Greece. Specifically, relevant data from six spectral responses are extracted to separate and quantify certain soil parameters such as clay content, NO3− levels, and SOM (Soil Organic Matter), despite their interrelations. We developed a method to visually represent the determination outcomes quantitatively by establishing three threshold parameter values: 1.6% for SOM, 19% for clay, and 13.6 ppm for NO3−. These determinations will span the explored ranges of ’0.3 to 4.18%’ for SOM, “3 to 48%” for clay, and ’0 to 661.2 ppm’ for NO3−. Utilizing supervised LDA proves notable classification rates of 93.3% for the SOM task, 90% for the Clay task, and 86.6% for NO3− with the specific cut-off value parameter. The final result is presented as a map showing the positions of various prediction samples, highlighting the percentages of clay and organic matter relative to 19% and 1.6%, respectively, as well as the nitrate concentration in these samples relative to 13.6 ppm.

APD: An automated parameter determination system based on in-situ tests

In-situ testing has numerous applications in geotechnical engineering. The interpretation of in-situ test results includes soil stratification and determination of soil parameters. This paper presents an automated parameter determination framework that aims to determine constitutive model parameters based on in-situ tests. The ongoing research project relies on a graph-based approach for determining the parameters. The framework has two main attributes: transparency and adaptability. Transparency is achieved by illustrating how a certain parameter was computed. Adaptability is ensured by allowing users to incorporate their expertise into the framework. The system currently determines parameters based on three main workflows that utilize the results of cone penetration tests, dilatometer tests, and shear wave velocity measurements. This study employs the three main workflows to determine soil parameters for one of the Norwegian GeoTest Sites. Additionally, the connection between the parameter determination system and finite element analysis is discussed, where the parameters for the Modified Cam Clay model are evaluated. The framework is valuable in the early stages of projects, providing detailed soil information when soil data is limited. Ongoing research aims to assess the accuracy of the derived soil and constitutive model parameters and to expand the system’s capabilities by including additional in-situ tests.

Evaluating of 14H-Piles’ Load Capacity in Cohesionless Soils for Edward’s Project Using ?-Method and Finite Element Method

Abstract: This paper presents a comprehensive study on evaluating the load capacity of 14H-piles in cohesionless soils for Edward's infrastructure project[1]. The project, overseen by the Federal Highway Administration (FHWA) and the Colorado Department of Transportation (CDOT), focuses on bridge replacements in Edwards, Colorado, with an emphasis on foundation improvements using driven pile foundations[2]. The research utilizes Finite Element Analysis (FEA) and the β-method to assess the total bearing capacity of 14H-piles in sandy soil conditions[3]. Field exploration, including Standard Penetration Tests (SPT), is conducted to determine soil parameters, followed by calibration of soil strength parameters. The study involves modelling the piles using the Soil-Structure Interaction 3-D program (SSI3D) and comparing the total bearing capacity obtained from FEA with nominal capacities calculated by the β-method. Additionally, the paper discusses load resistance and factor design, as well as the importance of static load testing for validation[4]. The results highlight the efficacy of FEA in predicting pile behaviour and provide valuable insights for foundation design in similar geological settings.

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

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

SOIL CHARACTERIZATION: INTEGRATING IN-SITU TEST, LABORATORY ANALYSIS, AND TERRAIN PARAMETERS

This research introduces an innovative methodology for characterizing geotechnical parameters of soils, crucial for designing foundations for various structures. Rather than relying on general correlations from geotechnical books, the primary goal is to obtain specific characteristic values tailored to the study area. The methodology establishes correlations between in-situ tests and laboratory tests to determine soil parameters in the Valley of the Chillos. The Valley of the Chillos is a geographical region in the southeastern part of Quito, Ecuador. It falls within the administrative zone of Los Chillos in the Metropolitan District of Quito. The valley is surrounded by the inactive Ilaló volcano to the north and the Pasochoa, Antisana, and Lomas de Puengasí mountains to the south, east, and west, respectively. To achieve the research objective, soil samples were collected using the Standard Penetration Test (SPT) at depths ranging from 1 to 15 meters, following the ASTM D-1586 standard. Additionally, undisturbed soil samples from the thin-walled Shelby tube were obtained in the field and subsequently tested in the laboratory. These tests covered aspects such as natural moisture content, granulometric analysis, Unified Soil Classification System, classification, liquid limit, plasticity index, specific gravity, void ratio, and degree of saturation. The results from both field and laboratory tests will be used to calculate the load capacity of foundations specifically for characteristic soils in the Los Chillos Valley, considering their classifications.

Determination of soil parameters using in situ soil testing for the design of highway

Determination of soil parameters of any soil is very important before executing highway construction as it gives detailed information of the soil and its various properties which will be useful in the design of the pavement. The paper discusses determination of soil parameters of the ongoing construction work, which includes grain size analysis, consistency test, compaction test, 4 day soaked CBR at 3 energy levels, and field details. Boreholes drilling was done using augers. Using rotary diamond (core) drilling equipment, cores were drilled into the bedrock, and bedrock core samples are then extracted from the cores. Test pits were excavated with hydraulic excavators. This paper summarizes the different geotechnical design parameters for the subsurface conditions at the site to support the highways. A review of rotary drilling and rock coring in terms of the correct handling, transportation, and storage of soil and rock samples in preparation for laboratory testing is also given.

Efficiency of cement and sand in stabilizing the black cotton soil

As the population rises and access to land decreases, an increasing number of buildings and other civil engineering structures must be constructed on weak or soft soil. Because of the soil's poor shear strength and substantial swelling and shrinking, a variety of ground improvement techniques, including soil stabilisation and reinforcing, are implemented to improve the mechanical behavior of the soil, hence boosting construction reliability. One of the most important soils is black cotton soil, which we intend to develop by utilising India's resources of sand and cement. In this research, several tests were conducted to determine the basic properties of soil. Instead of opting for the removal and replacement of unstable soil, soil modification is the only viable option because it saves both time and money. When exposed to changes in moisture content, they exhibit significant swelling and shrinking, making them the most challenging from an engineering standpoint. In this study, 2% cement was added at a steady rate while the sand percentage ranged from 10% to 40% which is utilised for the experiment was clean sand that had been passed through a 425 micron sieve and basic tests were performed to determine soil parameters. The physical properties of Black Cotton Soil determined & where the soil is of clay type & specific gravity is 2.6 and it is classified as CH. After determining the basic properties of virgin soil, the proctor test and CBR tests were performed by stabilizing with cement and sand. Sand is utilized in various percentages, such as 10%, 20%, 30%, and 40% of the total soil weight. MDD increased from 1.820 to 1.902 gm/cc. The CBR increased to 5.21% from 0.55% for 2.5mm penetration and 4.12% from 0.48% for 5mm penetration. As the percentage of sand increases, both thickness and cost drop. The CBR design chart yields a pavement thickness of 480mm for 20% sand and 2% cement, which is comparable to the minimum thickness of 475mm.

An automated system for determining soil parameters: Case study

The success of numerical analysis depends on several factors. One of the keys factors is the accurate determination of constitutive model parameters. Determining these parameters from in-situ tests has several advantages compared to laboratory tests, from lower costs to minimal disturbance of the soil. However, it is not possible to derive soil parameters directly from in-situ tests results, since correlations are required. The literature offers a wide range of correlations which increases the uncertainty during interpretation. The ongoing research project APD (Automated Parameter Determination) investigates the possibilities of automated parameter identification from in-situ tests using a graph-based approach. In the present paper, existing correlations – developed for cone penetration tests - are validated by comparing their output to laboratory results. The Norwegian GeoTest Sites (NGTS) infrastructure project consists of five tests sites in different soils in Norway. The data from the soft clay site located in Onsoy, south-eastern Norway was used in the validation process. A web-based application “Datamap” that has been developed to capture, organize, and classify geotechnical research data has been used to obtain in-situ and laboratory tests data. The further validation of existing correlations to derive accurate constitutive parameters from in-situ tests is part of ongoing research.

Determining parameters of peat soil for finite-element analysis using the Cam Clay model

A practical procedure for determining soil parameters when analysing the deformation of peat by finite-element analysis (FEA) using the Cam Clay model is presented. FEA has not been frequently used to predict the deformation of peat soil, even though it can be a powerful prediction tool. The main reason for this is that a practical method for determining parameters has not been established. In this paper, the remarkable correlation between the soil parameters required for FEA using the Cam Clay model and the natural water content (wn) and ignition loss (Li) of peat and organic clay is presented. Then, based on this correlation, a flow chart for determining the soil parameters of peat and organic clay is proposed. Using this chart, it is possible to determine soil parameters directly from triaxial compression, oedometer and other detailed tests or to estimate them from wn, Li and other physical indexes. A trial embankment on peat soil in Hokkaido, Japan, was also created. FEA was performed on the trial embankment using the proposed procedure for determining the soil parameters. The results of the FEA corresponded to the measured settlement behaviour in the trial embankment with an accuracy in the range −4% to +8%. The FEA results also modelled the measured behaviour of pore water pressure and lateral and vertical displacements in the trial embankment very well.

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson’s ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects.

Terramechanics models augmented by machine learning representations

The field of terramechanics focuses largely on two types of simulation approaches. First, the classical semi-empirical methods that rely on empirically determined soil parameters and equations to calculate the soil reaction forces acting on a wheel, track or tool. One major drawback to these methods is that they are only valid under steady-state conditions. The more flexible modelling approaches are discrete or finite element methods (DEM, FEM) that discretize the soil into elements. These computationally demanding approaches do away with the steady state assumption at the cost of including more model parameters that can be difficult to accurately tune. Model-free approaches in which machine learning algorithms are used to predict soil reaction forces have been explored in the past, but the use of these models comes at the cost of the valuable insight that the semi-empirical models provide. In this work, we presume that in a dynamic simulation, the soil reaction forces can be divided into a steady state component that can be captured using semi-empirical models and a dynamic component that cannot. We propose an augmented modelling approach in which a neural network is trained to predict the dynamic component of the reaction forces. We explore how this theory can be applied to the simulation of a soil-cutting blade using the Fundamental Earthmoving Equation and of a wheel driving over soft soil using the Bekker wheel-soil model.

Monitoring assessment of the low-pressure earthen dam of the Varnavinsky reservoir under conditions of an increasing risk of natural and man-made disasters

The need for increased attention to the reliability and safety of hydraulic structures in the water management complex of southern Russia is determined by the scale of the socio-economic consequences of their accidents. During the long-term operation of the low-pressure earthen dam of the Varnavinsky reservoir, various hidden defects and damages were formed. Which in the future can lead to a man-made disaster. The threat of catastrophic floods with the formation of hidden defects and damage will lead to natural disasters, namely the destruction of the dam body. The identification of these hidden defects and damages is a priority task in assessing the technical condition of the body of an earthen dam. The article considers only two methods of non-destructive testing for the detection of hidden defects and damage. Methods of electrocontact dynamic sounding and seismic exploration. They make it possible to obtain the electrical resistivity of soils when subdividing a section according to a lithological feature. As a result of data processing and interpretation, the geoelectric section of the electrical resistivity of soils was obtained. Applied hardware and methodological complex allowed to solve the tasks. The advantage of the complex is its methodological mobility. After a short experimental and methodological work and express processing, it is possible to clearly orient and combine the capabilities of the instrumentation complex and the engineering-geological situation in order to obtain the maximum quality and at the lowest cost of time and money. Studies of low-pressure earth dams should be carried out in the monitoring mode at various water levels in the reservoir. As can be seen from the results of processing, the geophysical complex of non-destructive testing makes it possible to study earth dams with the determination of soil parameters. To determine the physical and mechanical properties of soils, a greater amount of work should be carried out using reference drilling wells and identifying correlation dependencies of parameters.

Adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) modelling of Cu, Cd, and Pb adsorption onto tropical soils

Soils interact in many ways with metal ions thereby modifying their mobility, phase distribution, plant availability, speciation, and so on. The most prominent of such interactions is sorption. In this study, we investigated the sorption of Pb, Cd, and Cu in five natural soils of Nigerian origin. A relatively sparsely used method of modelling soil-metal ion adsorption, i.e. adaptive neuro-fuzzy inference system (ANFIS), was applied comparatively with multiple linear regression (MLR) models. The isotherms were well described by Freundlich and Langmuir equations (R2 ≥ 0.95) and the kinetics by nonlinear two-stage kinetic model, TSKM (R2 ≥ 0.81). Based on the values delivered by the Langmuir equation, the maximum adsorption capacities (Qm*) were found to be in the ranges 10,000–20,000, 12,500–50,000, and 4929–35,037 µmol kg−1 for Cd, Cu, and Pb, respectively. The study revealed significant correlations between Qm* and routinely determined soil parameters such as soil organic carbon (Corg), cation exchange capacity (CEC), amorphous Fe and Mn oxides, and percentage clay content. These soil parameters, combined with operational variables (i.e. solution/soil pH, initial metal concentration (Co), and temperature), were used as input vectors in ANFIS and MLR models to predict the adsorption capacities (Qe) of the soil-metal ion systems. A total of 255 different ANFIS and 255 different MLR architectures/models were developed and compared based on three performance metrics: MAE (mean absolute error), RMSE (root mean square errors), and R2 (coefficient of determination). The best ANFIS returned MAEtest 0.134, RMSEtest 0.164, and R2test 0.76, while the best MLR returned MAEtest 0.158, RMSEtest 0.199, and R2test 0.66, indicating the predictive advantage of ANFIS over MLR. Thus, ANFIS can fairly accurately predict the adsorption capacity and/or distribution coefficient of a soil-metal ion system a priori. Nevertheless, more investigation is required to further confirm the robustness/generalisation of the proposed ANFIS.

Hierarchical Bayesian modelling of quasi-region-specific soil porosity

Determination of soil parameters is considered crucial to the safety of marine geotechnical engineering. Due to the limited number of borehole samples that are usually available, the process of parameter estimation involves great uncertainty. A conventional practical approach is to conduct regression modelling of the region-specific data to develop an empirical equation, such as one that relates acoustic impedance (I) to porosity (n). However, most of the regions have insufficient measured data for such modelling. This paper proposed a porosity prediction method based on hierarchical Bayesian modelling (HBM), which aims to predict n in data-limited regions. A quasi-region-specific I–n relationship is established by simultaneously considering the measured data from other regions and the target region. Moreover, eight published I–n datasets from different regions are compiled to verify the proposed method. The comparative advantages and improvements in porosity estimates are discussed by comparison with two alternative models. From the extracted results, it is confirmed that the proposed method can predict n for specific regions with small datasets and effectively reduce the predictive uncertainty. Ten region-specific datapoints are sufficient to achieve a balance between the measurement cost and the predictive reliability.

Method for indirect determination of soil parameters for numerical simulation of dikes and earth dams

One of the most important steps in the numerical simulation of a hydrogeological system is the precise definition of initial and boundary conditions. The better these are characterized, the more efficient the calculation and the more accurate are the simulation result. In case of simulating processes in the unsaturated soil zone, the water retention curve, the relationship between volumetric water content and matric potential, is of great importance. However, the retention parameters determined locally by different standard methods often do not represent the whole soil system under consideration due to heterogeneities in the soil body caused by variability or different compaction of the soil. Resulting over- or underestimation of the parameters is leading to a worse performance of simulations of the water balance including to a higher calibration effort. Therefore, it is more favorable to identify these soil parameters by a method representing the whole soil system to avoid uncertainties. For this reason, a dike experiment was performed to investigate how soil parameters determined locally and globally can represent the properties of the whole soil system. When comparing the simulation results of the numerical models, a better agreement of measured and simulated water contents as well as a lower effort for calibration is observed by using the soil parameters determined globally.

Determination of Soil Parameters and Pulling Force Requirement of Tapioca Root

Aims: This paper aims to explore the soil parameters and pulling force requirement of tapioca root for the design and development of tractor operated tapioca harvester.
 Place and Duration of Study: The study was conducted to investigate the soil parameters and pulling force requirement of tapioca roots at farmer’s field in the year 2021-22.
 Methodology: The soil parameters viz. moisture content, bulk density, cone index and shear strength and pulling force requirement of tapioca root were studied using standard test procedure that plays an important role in design of soil engaging components and conveying unit of the tuber harvester.
 Results: The results showed that soil moisture content varied from 13.11 to 16.08 per cent, whereas bulk density ranged from 1483.07 to 1729.56 kg m-3. The cone index varied from 0.33 to 0.98 N mm-2 with an average value of 0.637 N mm-2 and shear strength ranged from 1.34 × 10-3 to 3.48 × 10-3 N mm-2 with the mean value of 2.46 × 10-3 N mm-2. The average value of pulling force requirement for tapioca roots was 103.4 kgf. The root depth and root yield per plant of tapioca roots has an average value of 250.30 mm and 7.82 kg.
 Conclusion: The study shows that bulk density of soil increased with increase in soil moisture content and had a linear relationship. The coefficient of determination (R2) for the regression model was 93.20 per cent by revealing the linear relationship between shear strength and cone index. Thus shear strength and cone index are related with each other and have an indefinite effect on the design parameters of soil engaging components of tuber harvester.

A Study of the Content of Pesticide Residue in Agricultural Soils Around Federal University Gusau, Zamfara State Nigeria

In this study, the assessment of levels of pesticide residue in agricultural soil in farmlands around Federal University Gusau was carried out. The objectives of this study are to determine soil parameters such as pH, moisture content, electrical conductivity, soil organic matter content, water holding capacity, identify the pesticide residues within the study area and assess the level of pesticide residue in the soil within the area. The sampling area covered arable farmland to the north, south, west and eastern boarders of the university campus. Analyses of cleaned sample extracts were performed using Gas Chromatography equipped with Flame Ionization Detector (GC-FID). A total of twelve agricultural soil samples were taken from the location at a depth of 20cm from the topsoil. The result obtained showed the presence of organochlorine and organophosphorous pesticides. Seventeen pesticide residues and derivatives were detected. These include; 2,4 dichloro, Hexachlorobenzene (HCB), Endosulfan, Aldrin, p,p' -DDD, g- chlordane, Profenofos, Carbofuran (a carbamate), DDVP, Dichlorvos, Heptachlor, t-nonachlor, Isopropylamine, Glyphosate, Biphenyl, Dichlorobiphenyl and Lindane. Physicochemical studies of the samples gave a pH that ranges from slightly acidic to slightly basic. The results when compared with IAEA/FAO/CODEX Alimentarius guideline are observed to be within the maximum residue limit. Keywords: Pesticides, Agricultural soil, Gusau , Gas chromatography, FAO DOI: 10.7176/JEES/12-7-04 Publication date: July 31 st 2022

Methodology of identification of deformation parameters of clay soil massif

This topic provides a comparison of the results, determined characteristics of clay soils in accordance with national standards and Eurocodes.
 In the work, two variants of calculations using numerical modeling using the finite element method are planned: 1) using soil characteristics determined in laboratory conditions; 2) using soil characteristics determined by field methods.
 When comparing the results of field and laboratory tests of clay soils, the reliability of their values was established in comparison with the most reliable standard methods.
 The test was carried out in accordance with national standards in field conditions using probes of various diameters, live and inventory piles and stamps with an area of 5000 sq.cm and 600 sq.cm. The obtained data may differ in magnitude from the results obtained according to EU requirements. Therefore, there is a need to compare the values of characteristics according to national standards and Eurocodes.
 Experimental studies were carried out at facilities in various cities of Ukraine. When choosing objects for comparison, attention was paid to the similarity of the geological origin of the soils. In the work, in part, the materials used by the department for determining the characteristics of soils in the past years were used. The article examines points from experimental objects of searches. Processed engineering and geological elements (IGE) of clayey soils that lie on the territory of the selected sites.
 A generalizing table of the averaged physical and mechanical characteristics of clayey soils by research objects is given and the results of tests of clayey soils by laboratory and field methods are given, which made it possible to establish the following: the dependences of the change in the modulus of deformation for sandy and clayey soils are excellent. The results of soil tests with stamps and an inventory test pile (ITP-127) are given, as well as their comparison. The use of ITP-127 in practice as a reliable method for determining soil parameters is substantiated.

Research on the Adhesive Performance of a Biomimetic Goat Hoof Track Shoe Pattern.

In this paper, reverse engineering technique was employed to extract the ridges of the hoof ball contour, and hoof ball tissue structure was analyzed based on the bionic prototype of goat hooves. The quantified geometric features were used to design the bionic track shoe pattern, which can enhance its adhesive performance and solve the problem that agricultural tracked vehicles in hilly and mountainous areas are prone to slip due to poor adhesive performance. The monolithic structure of the biomimetic goat hoof track shoe pattern and the ordinary one-line track pattern were arranged and combined; they included six kinds of track shoe models and the adhesive performance was compared. A discrete element system was established based on soil parameter determination to compare the maximum adhesion of different track shoe models. The bionic track shoe samples were prepared for soil bin tests to verify the reliability of the discrete element analysis results. Compared with the ordinary track shoe, the adhesion of the optimal bionic track shoe was improved by 9.1%.

Laboratory Testing of Kinetic Sand as a Reference Material for Physical Modelling of Cone Penetration Test with the Possibility of Artificial Neural Network Application.

Cone Penetration Testing (CPT) is a quick survey in situ method through which soil parameters are not determined directly, but have to be estimated using derived relations between required soil parameter and soil resistance at the testing probe. Boundary conditions affect the reliability of the estimated soil parameters, therefore controlled laboratory conditions were applied to the intended CPT procedure analysis. Density, pycnometry, oedometer and direct shear tests of kinetic sand were performed to prove its usability as a reference testing material for further CPT laboratory analysis. The results of testing the kinetic sand are presented in this paper. Executed tests proved the kinetic sand as a reliable material in terms of the homogeneity and consistency of its physical and mechanical parameters. The material is utilizable as a substitution of cohesive sandy soils in physical modeling without the negative impact of the consistency-dependent behavior of fine-grained soils. However, some differences in parameters with respect to the natural soils should be taken into account. Neural network theory and numerical approach will be applied to the intended CPT laboratory analysis under controlled boundary conditions using kinetic sand to evaluate its potential for the determination of soil parameters.