Estimation Of Physical Properties Research Articles

Effect of moisture on mechanical and physical properties of coals: a uniaxial compression study

The estimation of mechanical and physical properties of coal reservoirs is important for the successful exploration and development of coalbed methane (CBM). Unlike conventional sandstone reservoirs, coal reservoirs exhibit greater sensitivity to stress, resulting in distinct mechanical and physical behaviors. In this study, uniaxial compression tests were performed on both low-rank and high-rank coal samples under different moisture conditions to reveal the mechanical and physical property changes with stress. The results indicate that during axial stress loading (up to 2.8 MPa), axial strain initially increases rapidly and subsequently at a slower rate, with an axial strain of 0.13–0.25% observed at the maximum axial stress. The instantaneous Young’s modulus increases linearly before stabilizing, ranging from 618.01 to 4861.10 MPa, while the Poisson’s ratio remains relatively constant or increases linearly, ranging from 0.002 to 0.165. This results in a negative exponential decrease in both porosity and permeability, with maximum reductions of 1.77–4.21% and 5.38–12.25%, respectively. The mechanical properties of coal are influenced by both the cementation effect of water at low water saturation and the softening effect at high water saturation, which results in axial strain decreases and then increases as the water saturation increases. Concurrently, the elastic modulus initially increases and then decreases, while the Poisson’s ratio exhibits a less pronounced change or tends to increase. Consequently, there is a trend in which porosity and permeability first increase and then decrease. In addition, during stress unloading, the influence of water in coal induces a notable strain hysteresis phenomenon in water-containing coal samples, and this phenomenon is more obvious in the low-rank coals.

A theoretical approach based on machine learning for estimation of physical properties of LLDPE in moulding process

This study explores the prediction of mechanical characteristics of linear polyethylene based on oven residence time, employing various regression models and hyper-parameter tuning through the Whale Optimization Algorithm. The dataset comprises one input variable (oven residence time) and three output parameters (Tensile Strength, Impact Strength, and Flexure Strength). The models investigated include Multilayer Perceptron, K-Nearest Neighbors, Support Vector Regression, Polynomial Regression, and Theil–Sen Regression. The results showcased distinct performances across the models for each output parameter. The Polynomial Regression (WOA-PR) method has been identified as the most suitable option for predicting Tensile Strength due to its ability to achieve the lowest errors in terms of Mean Absolute Error, Root Mean Square Error, and Average Absolute Relative Deviation. K-Nearest Neighbors (WOA-KNN) outperforms other models in predicting Impact Strength due to its superior accuracy and reliability. Additionally, Support Vector Regression (WOA-SVR) emerges as the best model for predicting Flexure Strength, showcasing notable performance in minimizing prediction errors. These findings underscore the significance of model selection and optimization techniques in accurately predicting the mechanical properties of polymers, paving the way for enhanced manufacturing processes and material design.

Estimation of Physical and Electrical Properties of Various REBCO Tapes for Construction of Very-High-Field REBCO Magnet

Estimation of Physical and Electrical Properties of Various REBCO Tapes for Construction of Very-High-Field REBCO Magnet

AstroCLIP: a cross-modal foundation model for galaxies

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used – without any model fine-tuning – for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pre-training separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and specific-star-formation rate), we beat this supervised baseline by 19 per cent in terms of R2. We also compare our results with a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

Simulation and optimization of crude glycerol refining process as a co-product of biodiesel

Glycerol is an essential chemical raw material widely used in industries such as food, pharmaceuticals, cosmetics, tobacco, and textiles. With the increasing production of biodiesel, a considerable amount of crude glycerol co-product is generated. Purification of crude glycerol by separating it from biodiesel co-products can lead to the production of high-purity glycerol, thus enabling the synthesis of high-value-added chemicals to reduce the production cost of biodiesel. This paper proposes a new glycerol purification process based on the acidification of potassium oleate soap using sulphuric acid. The composition of crude glycerol is complex. The presence of salts can affect the binary interaction parameters of glycerol and water. The ion forms resulting from the ionization of potassium fatty acid soaps are not available in the database and require important data to be provided through physical property estimation. Additionally, the dissociation of sulphuric acid is involved in the system. These issues make it challenging to propose a complete process based on the characteristics of crude glycerol. In this study, the vapor–liquid equilibrium data of the glycerol-water system containing potassium sulphate under reduced pressure were experimentally determined, and the binary interaction parameters of the glycerol-water system were regressed and used in the subsequent process simulation. The entire process of crude glycerol refining from biodiesel co-products was simulated and optimized using Aspen Plus software, proposing an optimized complete refining process for crude glycerol and designing a crude glycerol refining process with a production scale of 100,000 tons per year. The potassium oleate soap is first acidified with sulphuric acid, then removed by centrifugation, with the resulting lower layer entering the distillation process. We compared two different distillation processes and identified the more optimal separation sequence. Glycerol products with a mass concentration of 99.2% were obtained, with glycerol and methanol yields of 98.83% and 99.79%, respectively. Based on the above research, energy integration and optimization of the process were further investigated through pinch analysis. Overall, energy savings of 7.192 Gcal/h were achieved, resulting in a reduction in energy consumption of 40.65%. This paper proposes a new purification scheme for crude glycerol and an energy integration scheme to reduce the purification cost of high-purity glycerol. This is significant for the rational utilization of crude glycerol, a co-product of biodiesel.

Correlation between thermal and density properties of chestnuts: preliminary results of experimental non-destructive testing

This study presents the preliminary outcomes of a methodology for the physical and mechanical characterization of various chestnut elements in different states of preservation. Strategizing conservation and retrofit interventions for timber is necessary, and to do this, it is necessary to establish an estimation of physical (transmissivity, thermal conductivity, humidity level, etc.) and mechanical properties (density, compressive or bending strength, etc.). This essential information is typically associated with timber defects, but there are lack of correlations. The primary objective is to establish correlations between thermal and density properties with the aim of preserving original assets. The investigation delves into the relationship between timber density and thermal properties through experimental non-destructive testing (NDT). Two NDTs were employed with the aim of correlating: penetrometric testing and active thermography investigations. The parametric study on the excitation period yielded valuable insights into the temporal dynamics of heat transfer within the timber, underscoring the significance of selecting appropriate excitation periods to capture precise thermal properties. Tabular data on relative humidity for salified, dried, and new samples provided a quantitative backdrop to these observations, unveiling the nuanced effects of humidity on the timber’s thermal response. The results of this study are positioned to inform future conservation efforts by laying the groundwork for a comprehensive understanding of timber’s mechanical properties. Particularly, the challenge lies in accurately estimating density, where surface tests are often less reliable than in-depth ones. Therefore, it is crucial to seek validation through other NDT tests, such as thermographic analysis and visual inspection, and hygrometric tests recognizing their importance in enhancing the reliability of density assessments. This approach will contribute to the development of more discerning preservation strategies.

Density measurements of homogeneous phase fluid mixtures comprising CO2/methanol and CO2/ethanol binary systems and correlation with equations of state

Equations of state (EoS) are powerful tools for estimating a wide variety of physical properties. However, the applicability of parameter sets derived from specific physical properties for the correlation and estimation of various other physical properties has received limited attention. Additionally, the estimation of physical properties using EoS is anticipated to be affected by the association of molecules. The densities of homogeneous phase fluid mixtures comprising carbon dioxide (CO2)/methanol (MeOH) and CO2/ethanol (EtOH) binary systems were measured in the current study using a high-pressure vibration-type density meter equipped with a circulation pump and a variable-volume viewing cell. Homogeneity was ensured by observing the fluid through the viewing window of the variable volume cell. The measurements were carried out at a temperature range of 313–353 K, the CO2 mole-fraction range of 0–80 mol%, and at pressures up to 20 MPa. Subsequently, the as-obtained experimental data were correlated with two EoSs, viz. Sanchez-Lacombe (SL) EoS and Perturbed Chain statistical associating fluid theory (PC-SAFT) EoS. The density correlations between SL and PC-SAFT EoS were almost identical in accuracy. Additionally, the association between CO2 and alcohols in PC-SAFT EoS had no discernible effect on the reliability of the density correlations. The vapor liquid equilibria (VLE) of the CO2/MeOH and CO2/EtOH mixtures were further estimated using parameter sets determined from the density measurements. Both the EoSs demonstrated comparable estimation accuracy; however, the pressure was estimated primarily near the critical region of the mixture, which yielded a lower estimation accuracy. Additionally, the densities of the binary systems were determined using characteristic parameters derived from the VLE correlations. Of the EoSs, the PC-SAFT EoS yielded a good correlation of the VLE, including the region near the mixture's critical region, while taking the association between CO2 and alcohols into consideration. Although few of the correlations were observed to be inferior, the density of the homogeneous fluid mixture was accurately estimated using the two EoSs, with the parameters obtained from the VLE correlations. The findings of the study thus suggest that in order to estimate the density and VLE using EoS-shared parameters, the parameter sets must first be determined using a VLE that exhibits a wide range of conditions affected by the system's associations.

Attenuation proxy hidden in surface brightness – colour diagrams

Aims. Large future sky surveys, such as the Legacy Survey of Space and Time (LSST), will provide optical photometry for billions of objects. Reliable estimation of the physical properties of galaxies requires information about dust attenuation, which is usually derived from ultraviolet (UV) and infrared (IR) data. This paper aims to construct a proxy for the far-UV (FUV) attenuation (AFUVp) from the optical data alone, enabling the rapid estimation of the star formation rate (SFR) for galaxies that lack UV or IR data. This will accelerate and improve the estimation of key physical properties of billions of LSST–like observed galaxies (observed in the optical bands only). Methods. To mimic LSST observations, we used the deep panchromatic optical coverage of the Sloan Digital Sky Survey (SDSS) Photometric Catalogue, Data Release 12, complemented by the estimated physical properties for the SDSS galaxies from the GALEX-SDSS-WISE Legacy Catalog (GSWLC) and inclination information obtained from the SDSS Data Release 7. We restricted our sample to the 0.025–0.1 spectroscopic redshift range and investigated relations among surface brightness, colours, and dust attenuation in the FUV range for star-forming galaxies obtained from the spectral energy distribution (SED). Results. Dust attenuation is best correlated with colour measured between u and r bands (u − r) and the surface brightness in the u band (μu). We provide a dust attenuation proxy for galaxies on the star-forming main sequence. This relation can be used for the LSST or any other type of broadband optical survey. The mean ratio between the catalogue values of SFRs and those estimated using optical-only SDSS data with the AFUVp prior calculated as ΔSFR = log(SFRthis work/SFRGSWLC) is found to be less than 0.1 dex, while runs without priors result in an SFR overestimation larger than 0.3 dex. The presence or absence of the AFUVp has a negligible influence on the stellar mass (Mstar) estimation (with ΔMstar in the range from 0 to −0.15 dex). Conclusions. We note that AFUVp is reliable for low-redshift main sequence galaxies. Forthcoming deep optical observations of the LSST Deep Drilling Fields, which also have multi-wavelength data, will enable one to calibrate the obtained relation for higher redshift galaxies and, possibly, extend the study towards other types of galaxies, such as early-type galaxies off the main sequence.

Estimation of Physical and Mechanical Properties of Portland Composite Cement Produced by Using Raw Perlite with Fuzzy Logic Model

Estimation of Physical and Mechanical Properties of Portland Composite Cement Produced by Using Raw Perlite with Fuzzy Logic Model

Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys

The thermophysical properties of liquid metals and alloys are crucial to explore the intrinsic mechanisms of the solidification process, glass formation, and fluid dynamics. The deep learning approaches have emerged as powerful tools in numerous scientific fields and exhibit extraordinary accuracy in the estimation of physical properties and structural characteristics for various materials. In this Tutorial, focusing on the thermophysical properties of liquid multicomponent alloys, deep learning methods, including both supervised learning and active learning, are introduced. Combined with the verification from electrostatic and electromagnetic levitation experiments, the influences of training parameters and methods on the accuracy to obtain interatomic potential by deep learning are revealed on the basis of deep neural network algorithm. As a result, this prediction method of liquid state properties for multicomponent alloys exhibited the dual advantages of high accuracy derived from density functional theory and low computational cost associated with empirical potential.

Estimation of physical and mechanical properties of high-strength concrete with polypropylene fibers in high-temperature condition

High-strength concretes (HSC) have been widely applied in civil construction. However, this material does not perform well in fire situations due to spalling, a phenomenon that can be significantly mitigated by adding synthetic fibers to HSC cementitious matrix. Estimating HSC physical and mechanical properties for different compositions and temperatures can therefore be useful, as the structure may be pre-designed without requiring prior material characterization in a fire situation. With this perspective, this paper aimed to propose regression models to estimate eight properties of High-Strength Concrete with Polypropylene Fibers (HSC-PPF), namely compressive strength, tensile strength, static modulus of elasticity, dynamic modulus of elasticity, ultrasonic pulse velocity, electrical resistivity, water absorption, and weight loss, at high temperatures (up to 800 °C). For this, it was evaluated the effective correlation between mechanical responses and independent variables, such as temperature, water absorption, and dynamic modulus, and the accuracy of regression models was assessed. The results obtained indicated that, among the models proposed, the two multiple regressions were better than the simple one for estimating HSC-PPF properties since they presented high adjustments (R2) and low mean absolute percentage (MAPE), and coefficient of variation (CV) errors. This methodology can be used in a wide variety of fiber-reinforced HSCs.

A New Approach for Estimation of Physical Properties of Irregular Shape Fruit

Dimensions, volume, and mass of agricultural goods are essential physical features to build sizing, grading, and packaging systems. Manual property measurements are time-consuming, costly, and labor-intensive due to traditional technologies while various devices need to be installed to adapt process requirements. Furthermore, it is very challenging to accurately measure product with irregular or specific shapes, such as starfruit (Averrhoa carambola). Recently, there have been several research results on estimating features of the irregularly shaped object, they are either get inaccurate results or need repeated captures, computational resources, and time to rebuild the three-dimensional representation of the goods. The starfruit has not been studied completely in this size and weight measurements. This paper focuses on new techniques which exhibit simple installation to generate multiple functions for estimating the dimensions, volume, and mass with high accuracy. In this proposed method, we separated the process into two main phases. In the first phase, a camera is used to capture a top-view image of a starfruit, then image processing and machine vision techniques are applied to process the acquisition image before the process slices numerically the starfruit into several pieces along the longitudinal axis and estimates the physical attributes of each pieces using disc method and conical frustum method. Its volume is the summation of the volume of each partial slice. In the next phase, the density is used to estimate the mass of starfruit since the correlation coefficient (R-squared) between the volume and mass of starfruit is nearly linear with 0.9205. The validated results are highly competitive with accuracy of about 99% for the volume and mass in 300 testing samples.

Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations

ABSTRACT In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, ‘supervised’ paradigm for the application of machine learning involves training a model on labelled data, and using this model to predict the labels of previously unlabelled data. The semi-supervised ‘pseudo-labelling’ technique offers an alternative paradigm, allowing the model training algorithm to learn from both labelled data and as-yet unlabelled data. We test the pseudo-labelling method on the problems of estimating redshift, stellar mass, and star formation rate, using COSMOS2015 broad band photometry and one of several publicly available machine learning algorithms, and we obtain significant improvements compared to purely supervised learning. We find that the gradient-boosting tree methods CatBoost, XGBoost, and LightGBM benefit the most, with reductions of up to ∼15 per cent in metrics of absolute error. We also find similar improvements in the photometric redshift catastrophic outlier fraction. We argue that the pseudo-labelling technique will be useful for the estimation of redshift and physical properties of galaxies in upcoming large imaging surveys such as Euclid and LSST, which will provide photometric data for billions of sources.

Fitting an active Brownian particle's mean-squared displacement with improved parameter estimation.

The active Brownian particle (ABP) model is widely used to describe the dynamics of active matter systems, such as Janus microswimmers. In particular, the analytical expression for an ABP's mean-squared displacement (MSD) is useful as it provides a means to describe the essential physics of a self-propelled, spherical Brownian particle. However, the truncated or "short-time" form of the MSD equationis typically fitted, which can lead to significant problems in parameter estimation. Furthermore, heteroscedasticity and the often statistically dependent observations of an ABP's MSD lead to a situation where standard ordinary least-squares regression leads to biased estimates and unreliable confidence intervals. Instead, we propose here to revert to always fitting the full expression of an ABP's MSD at short timescales, using bootstrapping to construct confidence intervals of the fitted parameters. Additionally, after comparison between different fitting strategies, we propose to extract the physical parameters of an ABP using its mean logarithmic squared displacement. These steps improve the estimation of an ABP's physical properties and provide more reliable confidence intervals, which are critical in the context of a growing interest in the interactions of microswimmers with confining boundaries and the influence on their motion.

Artificial Neural Networks and Its Applications in Chemical Industry

Artificial neural networks, as an important part of artificial intelligence, have a wide scope of development to improve the traditional production technology of chemical processes with its inherent advantages of parallel structure and parallel processing, fault tolerance, full approximation of any complex nonlinear relationships, learnability and self-adaptability, etc. Thus, it has a wide scope of development to improve the problems of lagging diagnosis, difficult to optimize control, large errors in physical property estimation and inability to deal with nonlinear complex situations. This paper summarizes the theory of artificial neural network, including its structure and characteristics, and introduces its applications in different fields, especially in chemical industry.

Improving FMCW GPR Precision through the CZT Algorithm for Pavement Thickness Measurements

Ground Penetrating Radar (GPR) application in road surface detection has been greatly developed in the past few decades, which enables rapid and economical estimation of pavement thickness and other physical properties in non-destructive testing (NDT) and non-contact testing (NCT). In recent years, with the rapid development of microwave and millimeter-wave solid-state devices and digital signal processors, the cost of Frequency-Modulated Continuous-Wave (FMCW) radar has dropped significantly, with smaller size and lighter weight. Thereafter, FMCW GPR is considered to be applied during pavement inspection. To improve the precision of FMCW GPR for NDT and NCT of pavement thickness, a Chirp Z-transform (CZT) algorithm is introduced to FMCW GPR and investigated in this paper. A FMCW + CZT GPR at 2.5 GHz with a bandwidth of 1 GHz was built, and laboratory and field experiments were carried out. The experimental results demonstrate that the FMCW + CZT GPR radar can obtain the sample thickness with low error and recognize subtle thickness variations. This method realizes the high precision thickness measurement of shallow asphalt pavement by FMCW radar with a narrow bandwidth pulse signal and would provide a promising low-cost measurement solution for GPR.

Broadband acoustic characterization of backscattering from a rough stratification surface

This work aims to elucidate the utility and limitations of broadband scientific echo sounders in characterizing the frequency-dependent scattering from stratification interfaces. A recently published 1D acoustic backscattering model for the oceanic stratification structure was expanded for this work to consider echo sounding applications and the importance of roughness (vertical and spatial variability) along the stratification interface. The expanded model was tested in utilizing data from a recent acoustic experiment in the Kattegat basin, a region with well-documented, strong, salinity-driven stratification. A suite of broadband split-beam echosounders (45–270 kHz) provided broadband backscattering data, which were validated using high resolution in situ observations of water column structure, collected with a CTD (Conductivity, Temperature, Depth) sensor and MVP (Moving Vessel Profiler). Overall, the frequency dependence of backscattering agrees with model predictions of a gradual decay in the reflected wave amplitude from stratification structure with increasing frequency. Stratification interface roughness drives changes in frequency-dependent scattering away from a smooth surface solution as a function of different roughness regimes, primarily defined by the root-mean-squared slope of the interface. Results suggest a path to remote estimations of physical medium properties through broadband acoustic inversion.

High-throughput informed machine learning models for ultrastrong B-N solids

High-throughput (HT) computations and machine learning (ML) algorithms are two fundamental approaches in data-driven paradigms to predict various properties of solids due to their efficiency in data creation and model construction, which however are usually used individually and lack generalization and flexibility. In this paper, we propose a scheme combining HT computations for the efficient creation of consistent data and ML algorithms for the fast construction of surrogate models to screen B-N solids' stability and mechanical properties at ambient and high pressures. Employing HT computations, a standardized database of formation enthalpy, elasticity and ideal strength of thousands of B-N structures with high precision is first established. Then several ML models are comparatively built employing the XGBoost approach with the consideration of four descriptors, i.e., sine matrix, Ewald sum matrix, SOAP, and MBTR. Our results suggest the MBTR provides more accurate estimates of various physical properties except for bulk modulus, which is evaluated with greater precision by the Ewald sum matrix. To further improve the model interpretability, based on the brittleness/ductility criterion of materials, a symbolic model with strong physical significance is successfully built for the ideal strength of the B-N solids through the key descriptors screened by the ML methods, showing great accuracy. Our research demonstrates the possibility of building high-efficient ML models and compact symbolic physical models by incorporating consistent data through HT computations for high accuracy in predicting the thermodynamic and mechanical properties of strong solids with high precision, providing a pathway for inverse design of novel materials.

SPT and CPT Correlation of Expansive Clay in Cikarang, Indonesia

Several CPT-SPT correlations have been reported worldwide to allow for the estimate of soil physical properties from one set of accessible data to another. Although most correlations are for silty and sandy soils, there is insufficient information on whether these correlations correspond to the silty clay soil conditions in Indonesia. Therefore, this study aims is to validate and enhance the generalized CPT-SPT correlation, with emphasis on Indonesian soil conditions to increase its prediction accuracy. The soil under examination is silty clay layers that cover most of Northern parts of West Java – Indonesia. Known with its expansive clay characteristics, these type of soils are sensitive to volume change as a result of seasonal variations in water content. For this study, data is collected from 8 (eight) locations in Cikarang Area. Each location consists of dedicated SPT and CPT pairs tests at 2 (two) m distance between each other. After analyzed with various statistical regression analysis of data relevant to this type of soil, a simple linear empirical CPT-SPT correlation with a fairly high correlation has been established allowing test findings to be translated and predicted for the relevant soils type. The simple CPT and SPT correlation is in form of n = qc/NSPT = 0.225 (Mpa), with data distribution of n ranges from 0.15 (Mpa) to 0.33 (Mpa). This results shows much lower n values compared to various correlation have been published worldwide. With respect to the clay soil formation, the low of n-value also reflect a lower density and cohesion bonding clay properties.