Anisotropic Resilient Modulus Model of Granular Materials Based on Particle Characteristics

2021 ◽  
pp. 036119812110237
Author(s):  
Zefeng Tao ◽  
Zengyi Wang ◽  
Jianming Ling ◽  
Yu Tian ◽  
Juewei Cai ◽  
...  
Keyword(s):  
Particle Size ◽  
Granular Materials ◽  
Particle Shape ◽  
Resilient Modulus ◽  
Design Procedure ◽  
Triaxial Tests ◽  
Model Parameters ◽  
Anisotropic Particles ◽  
Particle Characteristics ◽  
Shape Characteristics

Granular materials are widely used for bases or subbases in pavement structures. They typically exhibit strong anisotropic properties which relate to stress states and particle characteristics. The conventional design procedure for flexible pavements underestimates the anisotropy of resilient moduli. This study established an anisotropic resilient modulus model for granular materials that considered gradation and particle shape characteristics. Vertical and horizontal resilient moduli of certain granular materials were measured in self-developed triaxial tests to obtain corresponding model parameters and anisotropic coefficients. Gradation and particle shape models were established to quantify the granular material characteristics, and the parameters were regressed. Particle shapes were obtained via image processing, and the ratio ( η) of particle sphericity to roundness was chosen as a shape parameter. Results show that η increases with the decrease in particle size, and the average values of η for graded gravel and natural laterite are 0.54 and 0.63, respectively. The η distribution curves indicate that the proportion of relatively anisotropic particles, rather than extremely anisotropic particles, results in the differences in particle shape characteristics. The regression relationship between the anisotropic calculation parameters and the model parameters of vertical resilient modulus, gradation, and particle shape was established. Thus, the horizontal resilient modulus and the anisotropic coefficient can be predicted via conventional resilient modulus tests and gradation, and particle shape analysis. This study shows that the anisotropy of granular materials decreases with the increase in coarse particles and the uniformization of the particle size distribution, and it increases with the increase in anisotropic particles and the polarization of the η distribution.

scholarly journals Prediction Model of Minimum Void Ratio for Various Sizes/Shapes of Sandy Binary Mixture

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Chaojie Shen ◽  
Zhaoyang Xu ◽  
Jie Yin ◽  
Jinfeng Wu
Keyword(s):  
Particle Size ◽  
Binary Mixture ◽  
Particle Shape ◽  
Void Ratio ◽  
Soil Mechanics ◽  
Influence Factors ◽  
Natural Sand ◽  
Undetermined Coefficient ◽  
Shape Characteristics ◽  
Different Origins

The minimum void ratio is a fundamental physical index for evaluating particle properties in soil mechanics, ceramic processing, and concrete mixes. Previous research found that both particle size distribution and particle shape characteristics would affect minimum void ratio, while the current research generally uses a linear model to estimate the minimum void ratio of a binary mixture, ignoring quantitative effect of particle shape on the minimum void ratio. Based on a study of binary mixtures of natural sand from three different origins and iron particles of two different shapes, this paper analyzes the influence factors of the minimum void ratio, and a quadratic nonlinear model is proposed for estimating the minimum void ratio of binary mixture. The model contains only one undetermined coefficient, a, the value of which is correlated to the particle sphericity, particle size, and particle size ratio. A theoretical calculation formula for the coefficient a is proposed to quantitatively analyze the effects of these three factors on the size of the parameters. In the end, the model is used to estimate the minimum void ratios of sand and substitute particles from different producing areas; the average difference between the estimated values and the fitted values is about 2.03%, suggesting that the estimated values of the model fit well with the measured data.

scholarly journals An Evaluation of the Resilient Modulus and Permanent Deformation of Unbound Mixtures of Granular Materials and Rubber Particles from Scrap Tyres to Be Used in Subballast Layers

2016 ◽  
Author(s):  
Carlos Hidalgo Sgnes
Keyword(s):  
Granular Materials ◽  
Resilient Modulus ◽  
Permanent Deformation ◽  
Waste Material ◽  
Triaxial Tests ◽  
Rubber Content ◽  
Cyclic Triaxial ◽  
Cyclic Triaxial Tests ◽  
Vibration Attenuation ◽  
Rubber Particles

Over the last years rubber from scrap tyres has been reused in different civil works such as road embankments and railway platforms due to its resilient properties, low degradation and vibration attenuation. Unfortunately, this issue is still scarce. For instance, in Spain about 175.000 tonnes of scrap tyres were collected in 2014, of which only 0.6% were reused in civil works. Aiming to contribute to the reutilisation of large quantities of this waste material, this paper focuses on the analysis of unbound mixtures of granular materials with different percentages of rubber particles to be used as subballast layers. Mixtures are tested under cyclic triaxial tests so as to obtain their resilient modulus and evaluate their permanent deformations. It is found that as the rubber content increases, the resilient modulus decreases and the permanent deformation increases. Taking into account the usual loads transmitted to the subballast layer, the optimum rubber content that does not compromise the behaviour of the mixture is set in a range between 2.5% and 5% in terms of weight.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4231

scholarly journals Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

2021 ◽  
Author(s):  
Ignacio González Tejada ◽  
P. Antolin
Keyword(s):  
Particle Size ◽  
Mechanical Behavior ◽  
Granular Materials ◽  
Triaxial Compression ◽  
Training Data ◽  
Model Parameters ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Hidden Correlations ◽  
Dense Packings

AbstractA data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress–strain curves were fitted to Duncan–Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.

scholarly journals Impact of sample scaling on shear strength: coupled effects of grains size and shape

2021 ◽  
Vol 249 ◽  
pp. 06011
Author(s):  
Sandra Linero-Molina ◽  
Emilien Azéma ◽  
Nicolas Estrada ◽  
Stephen Fityus ◽  
John Simmons ◽  
...  
Keyword(s):  
Particle Size ◽  
Shear Strength ◽  
Granular Materials ◽  
Particle Shape ◽  
Contact Dynamics ◽  
Material Strength ◽  
Particle Sizes ◽  
Size And Shape ◽  
Particle Size And Shape ◽  
Geotechnical Testing

Size limitations of geotechnical testing equipment often require that samples of coarse granular materials have to be scaled in order to be tested in the laboratory. Scaling implies a convenient modification of the particle size distribution (PSD) to reduce particle sizes. However, it is well known that particle size and shape may be correlated in nature, due to geological factors (as an example). By means of two-dimensional contact dynamics simulations, we analyzed the effect of altering the size span on the shear strength of granular materials when particle size and shape are correlated. Two different systems were considered: one made of only circular particles, and the second made of size-shape correlated particles. By varying systematically the size span we observed that the resulting alteration of material strength is not due to the change in particle sizes. It results instead from the variation of the particle shapes induced by the modification of the PSD, when particle size and particle shape are correlated. This finding suggests that particle shape distribution is a higher order factor than PSD for the shear strength of granular materials. It also highlights the importance of particle shape quantification in soil classification and the case for its consideration in activities such as sampling, subsampling, and scaling of coarse materials for geotechnical testing

scholarly journals Shape Characteristics of Coral Sand from the South China Sea

2020 ◽  
Vol 8 (10) ◽  
pp. 803 ◽  
Author(s):  
Xing Wang ◽  
Yang Wu ◽  
Jie Cui ◽  
Chang-Qi Zhu ◽  
Xin-Zhi Wang
Keyword(s):  
Particle Size ◽  
Bulk Density ◽  
Quartz Sand ◽  
Particle Shape ◽  
Fractal Theory ◽  
Skewed Distribution ◽  
Shape Parameters ◽  
Coral Sand ◽  
Particle Images ◽  
Shape Characteristics

The particle shape of coral sand is a crucial factor that affects its accumulation characteristics. Two-dimensional particle images of coral sand with different particle sizes were obtained through optical imaging, and the basic size parameters of particles were measured by digital image processing. The particle shape parameters were created, and on this basis, the variation of shape parameters with size, the distribution characteristics, and the sensitivity of shape parameters were analyzed by mathematical statistics and the fractal theory. In addition, a comparative analysis was conducted for the particle shape and bulk density of coral sand and quartz sand with the same particle size. The results show that (1) for coral sand with particle size ranging from 0.5 to 5.0 mm, as the particle size augments, its overall profile coefficient grows, while the flatness, angularity, and roughness diminish and the particle shape deviates more from the regular circle. (2) The shape of coral sand particles exhibits good fractal characteristics, and the particle shape gets more complex as the particle size grows as evidenced by the fact that the fractal dimension enlarges. (3) All the shape parameters obey a skewed distribution. Concerning the sensitivity to the change in particle shape, the flatness occupies the first place, the overall profile coefficient and angularity come second, and the roughness ranks third, accordingly. It is suggested that flatness should be preferred as the evaluation parameter of the particle shape. (4) Compared with that of quartz sand, the particle shape of coral sand is more irregular, and the intergranular pores are larger under the same accumulation conditions, which is the primary reason why the specific gravity of coral sand is greater than that of quartz sand while the bulk density is smaller than that of quartz sand.

scholarly journals Estimating Contact Force Chains Using Artificial Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6278
Author(s):  
Mengmeng Wu ◽  
Jianfeng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Granular Materials ◽  
Coordination Number ◽  
Contact Force ◽  
Force Chains ◽  
Granular System ◽  
Biaxial Test ◽  
Artificial Neural

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.

Modelling Particle Size Distribution of Residual Fly Ash from Pulverized Biomass Combustion

2021 ◽  
Vol 15 (1) ◽  
pp. 75-82
Author(s):  
Mingzi Xu ◽  
Changdong Sheng
Keyword(s):  
Particle Size ◽  
Fly Ash ◽  
Simple Model ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Particle Shape ◽  
Great Influence ◽  
Biomass Combustion ◽  
Ash Formation ◽  
Residual Ash

The present work aims to develop a simple model for describing the particle size distribution (PSD) of residual fly ash from pulverized biomass combustion. The residual ash formation was modelled considering the mechanism of fragmentation and coalescence. The influences of particle shape and stochastic fragmentation on model description of the PSD of the fly ash were investigated. The results showed that biomass particle shape has a great influence on the model prediction, and a larger fragmentation number is required for cylindrical particles than that for spherical particles to get the same PSD of fly ash, and the fragment number of the particles increases with the shape factor increasing. For pulverized biomass with a wide size distribution, the model predicted ash PSD considering the stochastic fragmentation is very similar to that assuming uniform fragmentation. It implies that the simple model assuming uniform fragmentation is applicable for predicting fly ash size distribution in practical processes where biomass particles have a wide range of sizes. For the fuel with a narrower initial PSD, the stochastic fragmentation model generally predicts a coarser PSD of the residual ash than assuming uniform fragmentation. It means the stochastic fragmentation is of great influence to be considered for accurate description of ash formation from the fuel with a narrow PSD.

scholarly journals A Methodology for Determination of Resilient Modulus of Asphaltic Concrete

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
A. Patel ◽  
M. P. Kulkarni ◽  
S. D. Gumaste ◽  
P. P. Bartake ◽  
K. V. K. Rao ◽  
...  
Keyword(s):  
Resilient Modulus ◽  
Triaxial Tests ◽  
Asphaltic Concrete ◽  
Empirical Relationships ◽  
Cyclic Triaxial Tests ◽  
Electronic Circuitry ◽  
Quality Check ◽  
R Value ◽  
Vast Variation

Resilient modulus, , is an important parameter for designing pavements. However, its determination by resorting to cyclic triaxial tests is tedious and time consuming. Moreover, empirical relationships, correlating to various other material properties (namely, California Bearing Ratio, CBR; Limerock Bearing Ratio, LBR; R-value and the Soil Support Value, SSV), give vast variation in the estimated results. With this in view, an electronic circuitry, which employs bender and extender elements (i.e., piezo-ceramic elements), was developed. Details of the circuitry and the testing methodology adopted for this purpose are presented in this paper. This methodology helps in determining the resilient modulus of the material quite precisely. Further, it is believed that this methodology would be quite useful to engineers and technologists for conducting quality check of the pavements, quite rapidly and easily.

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