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The Indian Concrete Journal December 2017 51

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Need of an efficient particle size analysis and its influence on properties of concrete

Athira Gopinath, Bahurudeen A., Akilesh Ramesh and Naveen Kumar

Although requirement of high strength concrete is extremely high in modern concrete construction, the ways to achieve that are still very expensive and unsustainable. The concept of particle packing dates back to the 19thcentury, however its practical use in concrete is still atypical. Concrete mixes are proportioned by considering all the characteristics of the ingredients required and its performance in fresh as well as hardened states. The performance of concrete in terms of its strength, workability and durability is mainly dependent on the proportioning and degree of packing of its ingredients. Hence, the particle-packing concept must be well known to the concrete mix designers to enable them in selecting the right proportion of materials. Present mix design involves the sieve analysis of aggregates and does not consider more in-depth specifications to achieve proper packing efficiency. Moreover, utilization of chemical admixtures, fine supplementary cementing materials such as silica fume, metakaolin, cement with high fineness and fillers are common in modern concrete. Therefore, knowledge on optimal packing with minimum void content, less permeability and high strength concrete is imperative. This paper describes modern sophisticated analytical techniques used for particle size analysis and their potential to be used for characterizing the fineness of concrete ingredients. Moreover, influence of particle packing on concrete characterises is comprehensively reviewed at the later part of the study.

INTRODUCTIONFresh and hardened characteristics of concrete entirely depend upon the quality as well as quantity of basic constituent materials such as cement, water, fine and coarse aggregates. The basic objective of concrete mix design is

to attain required strength, workability and durability economically. Although concrete mix is designed by certain proportions of various ingredients, these ingredients are in turn sub-designed by mixing various sizes to form the ideal concrete mix. As the mix up of different size ranges of aggregate will definitely affect the strength and cost of concrete, it must receive utmost attention. Thus alternative mix designs are necessitated, which would produce an efficient and cost effective concrete. Instead of single sized aggregate, the alternative approach includes different grades of aggregate selection and their combination. Moreover, supplementary cementing materials and filler play an essential role on strength and particle packing in modern concrete. The basic objective of such alternate approaches is to attain the lowest void content and the maximum packing density. As void content reduces, the coarse aggregates and fillers are used effectively, leading to lesser consumption of cement. It would cause a large reduction in the carbon footprint due to reduction in volume of cement, thereby forming an eco-friendly concrete. Moreover, reduction in void spaces results in decreased shrinkage. Thus, a scientific and systematic design mixed concrete with high performance and cost effectiveness is obtained.

Recent advances in mix-design methods are based on the requirements of maximum density gradation, using the ‘Particle-packing’ concept to obtain the maximum packing density by optimization of aggregates in concrete. This concept was initially proposed by Feret in 1892, suggesting that the maximum strength could be achieved when the void spaces within the matrix is minimal. An efficient, eco-friendly and cost-effective concrete could be obtained by

The Indian Concrete Journal December 201752

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optimization of cement paste composition and aggregate skeleton. The paste composition would be selected based on the rheological properties and the aggregate skeleton is to be determined for reduction of void spaces between the particles, thereby increasing the density. Wong et al. observed that the rheological properties of concrete could be improved by increased packing density of blended cement concrete (includes fly ash and slag) [1]. Nevertheless, Kwan and Wong found that, while maximum packing density would result in increased strength properties, it does not necessarily result in the required flowability characteristics. The rheological properties of cement paste would play considerable role in maintaining the workability properties and should be taken in to consideration simultaneously with the maximum packing density concept [2].

Packing optimization plays a major role in the improvement of concrete performance. It was analysed by the basic principle that dimensional stability of concrete could be improved by reducing the cement content or the paste volume [3]. In order to reduce the cement content usage, it has been in practice to replace the cement partially with alternative cementitious materials such as silica fume, fly ash etc. Earlier studies reported improved performance in terms of shrinkage and strength [4]. In addition, the void spaces within the blended cement would be reduced contributing to less permeability and strength. Blended cement with different types of cementitious materials would produce self-consolidating concrete up to grade 100 by maximizing packing density [5]. Hence, it is clear that particle packing plays a vital role in governing the various attributes of concrete. Therefore, to achieve best design mix proportions and optimised packing density, precise particle-packing models are required.

The concept of particle packing technology has been applied in several fields such as asphalt, ceramics, powder technology etc. and is of wide interest in the study of such materials. However, the technological advancements in this area has not yet found its way to concrete mix proportioning. The concrete composition depends not only on the aggregate grading but also on the packing of fine materials such as cement, fly ash, silica fume, etc. To understand how these particles get packed in a system, different particle size analysing instruments and packing models need to be considered. These particle-packing models select the suitable small size materials and their proportions to fit into the large void spaces. Further, smaller sized particle composition is determined to fit the voids of the particles selected, and so on, thereby enabling minimum porosity of concrete. This concept is very well explained in the Figure 1.

For the purpose of optimization of concrete composition, several particle-packing models have been proposed in the existing literature. However, these models can be made use of only if accurate particle size of all the ingredients is measured. With the advent of new and alternative supplementary cementitious materials, which are very fine in size, sophisticated particle size determination has become very important. Salient measurement techniques used to find the particle size of such fine particles and effect of particle packing on performance properties of concrete are discussed in the paper.

SOPHISTICATED PARTICLE SIZE DETERMINATION TECHNIQUES Many particle size analysers have been used to determine the particle size as well as the particle size distribution (PSD) of different solids. Prominent particle size analysing techniques are detailed below.

Laser Diffraction Analysis

When a laser beam is passed through material, it is diffracted and these diffraction patterns are used for measuring the particle size known as laser diffraction analysis. This analysis is based on Fraunhoffer diffraction theory, which states that the intensity of light diffracted by a single particle is directly proportional to the particle size. However, the diffraction angle of the laser beam maintains an inverse relationship with the particle size i.e., with the increase in the size of the particle, the angle of laser beam decreases and vice-versa. Either Fraunhoffer or the Mie theory is used for transforming the data into the particle size distribution. Fraunhoffer theory is used for large particles whereas Mie theory can be used for higher accuracy. The sample must be dispersible and should not settle down. Sonification is a common error in this technique, which has to be accounted.

Cyr et al. reported two sources of errors i.e., selection of the mathematical model and the evaluation process of optical properties of materials [7]. It is concluded that the

Figure 1. Particle Packing Concept [6]


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selection of mathematical model could be based on three main parameters i.e., (i) the absolute ratio between the real parts of the refractive indices of the material and medium, (ii) the size of particles, and (iii) their absorption. Ferraris et al studied about the factors influencing the PSD of cement by laser diffraction spectrometry (LDS) [8]. The particle size distribution was found influenced by dispersion efficiency, solids concentration, selection of dispersion medium, and the application of mechanical or chemical de-agglomeration methods. Another error source originates from conversion of optical spectrum to PSD which requires the inputs of optical constants i.e., real and imaginary parts of refractive index of solid phase whereas cement is a multi-phase material. In order to improve the precision of LDS and degree of confidence, the size distribution of various components within a multicomponent powder can be resolved. For example, the particle size distribution of gypsum within cement powder could be determined by measuring the difference between cement mixed with isopropyl alcohol and benzyl alcohol. Ferro et al conducted a comparative study between laser diffraction method (LDM) and sieve hydrometer method (SHM) by testing about 228 soil samples representing a different texture classification [9]. The experimental studies showed that the sand content measured by SHM was nearly similar as that of LDM, while the clay portion measured by LDM was slightly lesser than that of SHM. The distribution by USDA texture by SHM and LDM is shown in Figure 2.

Dynamic Light Scattering (DLS)

It is also recognized as Photon correlation spectroscopy (PCS) or quasi-elastic light scattering. It is a light scattering technique used to measure the size distribution profile of small particles in suspension. A laser beam is illuminated onto the sample and the light gets scattered, whose fluctuations are measured at known scattering angles by using a fast photon detector. This temporal variation is analysed to determine time decay constant of auto correlated function (ACF) thereby determining the diffusion coefficient and the size information. DLS measures the scattering pattern produced when light is passed through a sample. Decay rate constant and diffusion coefficient are directly proportional as shown in equation below:

Г = q2* D

where ‘q’ is a wave vector, independent of the scattering angle.

Now, the radius of the particle (R) is related to diffusion coefficient (D) by means of Stokes-Einstein equation

where T-temperature, KB – Boltzmann constant and η - Viscosity

Figure 2. Distribution by USDA texture using the percentage of clay, sand and silt from (a) Sieve Hydrometer Method (b) Laser Diffraction Method [9]


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To avoid multiple scattering and particle-particle interactions, the particle concentration should be very low that the mean distance between any two particles is 20 times the diameter of each particle. Sanghoon et al. determined the particle size distribution using dynamic light scattering technique [10]. It was finally concluded that the dynamic light scattering technique could be applied well to various mineral particles to determine the PSD at any condition. Since the technique is used to draw the size profile of particles in suspension in the range of nanometres, it can be used for finding out the particle sizes of the suspended solids in the chemical admixtures used in cement concrete.

Coulter Counter (CC)

In this technique, the size and number of particles are determined based on the changes in electrical resistance produced by the non-conductive particles that are suspended in an electrolyte. The sample in the beaker is placed on a platform. An aperture is made in a glass orifice tube at a lower level; the tube is also filled with the same sample. Two electrodes are immersed on either side of the orifice and a controlled suction pressure is applied to the orifice.

The particle passage causes an increase in the resistance to current flow, which is then detected as voltage pulse, which is proportional to the particle volume. Using the coulter’s counter and pulse analyser’s circuit, the number and the size of the particles passing through the sensing zone could be measured. Figure 3 shows the schematic diagram of the Coulter principle. This technique was adopted to find particle size distribution of cement and reported significant

agreement with observations from Blaine’s air permeability test [12].

Scanning Mobility Particle Sizer (SMPS)

This method is based on the electrical mobility of particles charged by passing through a generated electric field. Using

Figure 3. Schematic Diagram of Coulter principle [11]

Figure 4. Schematic diagram of scanning mobility particle sizer [13]


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a radioactive source, the particles are neutralised such that they attain equilibrium charge distribution as per Fuchs theory.

These neutralised particles are passed through the differential mobility analyser (DMA) where they are classified based on their electrical mobility as represented in Figure 4. The particle size is determined based on electric charge, central rod voltage and flow within DMA. In case of SMSP, full particle size distribution could be obtained by scanning the voltage rod exponentially. SMPS and DMA are quite useful particle size analysing techniques with wide range of applications in fine filler particles. It is an effective tool to find out the particle size distributions of multi-walled carbon nanotubes [13], fumed silica etc. which are widely adopted in concrete systems to produce ultra-high strength concrete. Trostl et al. worked on accurate measurement of particle size lesser than 20 nm using a new instrument TSI classifier (TSI 3082) to check the performance of Nano-SMPS in combination [14].

Scanning Electron Microscopy (SEM)

It is a technique used for attaining high-resolution images of small size particles, where electron beams are made to pass through the sample to produce images of the sample. The resolution of SEM is generally of the order of about 2 nm whereas the resolution of a conventional optical microscope is just 1μm.

Mohammed A et al. determined the physical and chemical characteristic of fly ash using Automated scanning electron microscopy technique (ASEM) [14]. The study characterized fly ash particle based on ASEM technique and determines the particle shape and structure. Figure 5 illustrates the

ASEM technique. The accuracy of ASEM techniques were checked by scanning the sample three times and average deviation was found to be less than 3% in case of diameter, X-ray counts and area. Thereby a consistent particle size distribution (PSD) was determined. Cherian et al. developed a new methodology for determination of grain size as well as pore size distribution of geo-materials such as fine grained soil, coarse grained soil, rocks and geosynthetics accurately using digital image analysis [15]. Some grain-size analysis experiments have been conducted on selected samples of Indian standard grade III sand using a Java based image processing and analysis program, “ImageJ” and is shown in Figure 6.

The conventional techniques were found to have limitations as it considers spherical shape of particle and therefore, the grain size distribution of flaky as well as elongated particles are highly subjective. It was found that the automated digital image analysis has higher capabilities for analysing microstructural features and deriving the relevant information i.e., shape, size, orientation and relative distribution of grains as well as pores etc. with high precision. However, care should be taken in fixing a threshold value since a value too high or too low may lead to loss of information.

Nanoparticle Tracking Analysis (NTA)

This method analyses the particles in a solution, which relates particle size to the Brownian movement concept. In this method, the tiny particles in suspension are illuminated by a thin laser beam and the scattered light is visualized using an ultra-microscope where the Brownian motion of particles can be observed. This technique provides particle

Figure 5. Illustration of ASEM Technique [14]


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size distribution of the very fine particles along with a real-time view.

To increase accuracy, it could be merged with other techniques like PCS (Photon correlation Spectroscopy). Filipe et al. made a comparison between dynamic light scattering (DLS) and nano particle tracking analysis (NTA) (Figure 7) by testing its performance [17]. It was observed that NTA gave results that are more accurate while analysing the mono disperse as well as poly disperse samples. Cementitious systems are constantly expanding to new realms to attain higher strength and better properties, the recent attempt being the incorporation of nano-materials such as carbon nanotubes [18] and Nano-silica [19] in the concrete matrix. This technique could be effectively used to find out the particle size distribution of these materials to achieve more accurate particle packing.

PARTICLE PACKING MODELSThe basic concept of particle packing models is that the void spaces between the large particles should be filled by smaller particles, the void spaces between these smaller particles is to filled by further smaller particles and so on, by which a minimized porosity or increased packing density is attained. These particle-packing models can be classified as Discrete Models and Continuous Models as represented in Figure 8.

DISCRETE MODELSThe primary assumption is that each class of particle needs to be packed to its full volume to attain the maximum density. These discrete models are further classified as binary mixture model, ternary mixture model and multimodal mixture model

Binary Mixture Models

The binary particle packing theory was initiated by Furnas [21]. The basic concept of Furnas model is that the interstices between the large sized particles are to be filled by the smaller particles without disturbing the large sized particles. It was primarily meant only for sphere shaped particles. Furnas considered a mixture of two materials i.e., fine aggregate and coarse aggregate for particle packing. Their volumes are expressed in terms of its partial volumes; ϕi , which means volume occupied by class ‘i’ group of particles in a unit volume. In addition, depending on the volume fraction of these materials, two different cases were considered.

• Volume fraction of coarse grain particles is far higher than the fine grain particles

• Volume fraction of fine grain particles is much higher than the coarse grain particles

Figure 6. Sequence of SEM micrograph analysis using ImageJ tool [15]


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When the diameters of the coarse and fine aggregates are comparable to each other, then interaction effects such as Wall Effect and Loosening Effect occurs. These are explained in Figure 9.

After the Furnas model, Power model [22] was designed which derived a formula to determine the minimum void ratio of binary mixture considering the wall and loosening effect. Aim and Goff [20] have proposed a model that takes into account the excess voids, and considered a correction factor for the wall effect in calculation of the packing density of the binary mixture.

Ternary Mixture Models

Ternary Mixture Model is an extension of binary mixture models suggested by Toufar et al. [23]. It was assumed that small particles are not fine enough to fit into the spaces of large particles in this model. The packing density of the ternary mixtures is calculated as a weighted average of binary models with diameter ratio between 0.22 and 1.0. It can be categorized into different cases: (i) A mixture Figure 7. Illustration of Nanoparticle Tracking Analysis [16]

Figure 8. Classification of Particle Packing Models [20]

Figure 9. Wall Effect and Loosening Effect [20]


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of packed areas mainly consisting of large particles and (ii) Packed areas mainly consisting of smaller particles with discrete large particles placed in the matrix of small particles. The multi-component system is considered as a combination of many binary mixtures formed of any two materials. Goltermann et al [24] proposed a modification in Toufar model which is popularly known as ‘Modified Toufar Model’. In this model, each different component has a packing degree factor, termed as Eigen packing which is determined according to the work’s procedure. Goltermann et al compared the packing values obtained by Aim, Toufar and modified Toufar packing models with experimental values obtained by the binary mixtures [24]. It was found that the modified Toufar model was very close to the measured packing degrees.

Multi-Component Mixture Models

Centred on the properties of multimodal discrete sized particles, Larrard had proposed three different approaches for the design mix proportioning of concrete, namely Linear packing density model (LPDM), Solid Suspension Model (SSM) and Compressible Packing Model (CPM)

Linear Packing Density Model (LPDM)

Stovall et.al proposed this model and packing density could be determined using the PSD data as well as the size of particle [25]. Later, determining the Eigen packing density as well as the particle size distribution of different combinations of each size class ‘i’ was proposed. Moreover, superplasticised cementitious materials were considered in the model.

Solid suspension model (SSM)SSM was proposed by Larrard and Sedran, with slight modification from LPDM. It is found to be a more valuable

tool in determining the high packing density of cementitious materials. Virtual packing and actual packing density were used. Virtual packing density is the density achieved when original particles are arranged in the mixture to get maximum density. It is also expected that this model would be able to predict the plastic viscosity of suspended mixtures.

Compressible packing model (CPM)

Proposed by De Larrard, this model is not dependent on either LPDM or SSM. It considers a compaction index ‘K’ to determine the actual packing density from the virtual packing density. It follows the same process as of LPDM to calculate the packing density; however, the virtual Eigen packing density value changes for different compaction indices.

Dewar Model

This model has a straightforward stepwise calculation of void ratios of two fine particles and then find for coarse particles. Moreover, it considered two parameters accounting for the loosening and wall effects, and hence it is called as the 2-parameter model. Kwan et al (2013) studied the packing density of glass beads experimentally using 2-parameter model [26]. As seen in the Figure 10, a peak at the optimum volumetric fraction provides the maximum packing density.

It can be seen from the figure that the variation occurs when the packing density is maximum. This difference is a because of a structural effect called “Wedging Effect”, as illustrated in Figure 11.

CONTINUOUS MODELSContinuous models assume that all the particle sizes would be present in a particle-size distribution system. It means that the entire class size ratio in case of discrete approach would reach 1:1, thereby maintaining no gap between the adjacent size classes.

Figure 10. Packing density against volumetric fraction of fine particles [26]

Figure 11. A Schematic diagram representing wedging effect, loosening effect, and wall effect [26]


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Fuller and Thompson Model

It is an effective and old model proposed by Fuller and Thompson (1907) [27]. It proposed properties of concrete could be improved by continuous grading of aggregates. Moreover, gradation curves for attaining maximum density was suggested which is known as Fuller’s “ideal” curve. These curves are mostly used in pavement mixture design. It is explained by a simple equation:

CPFT =(d/D)n 100

where, CPFT = Cumulative percentage finer than (by volume); n = 0.5, later this value is revised to 0.45; d = Particle size; D = Maximum particle size.

Shakhmenko and Birsh [28] have modified this expression for concrete mixtures proportioning, where Cumulative percentage finer than (CPFT) is expressed as follows:

CPFT = Tn(di /Do )n

where, n = degree of an “ideal” equation curve. Tn = Coefficient based on exponent ‘n’ and maximum aggregate size.

Andreassen Model

Andreassen et al. worked on the particle packing theory based on the continuous approach of particle size distribution [23]. The main assumption in the model was that all the small particles considered are infinitesimally small and proposed an Andreassen equation for ideal packing. Later, Dinger and

Funk have made a modification to Andreassen model [29]. They considered the finest particles to be finite enough and modified the equation proposed by Andreassen. Further, a model was proposed which links the Andreassen and Furnas distributions and termed it as AFDZ equation (Andreassen, Funk, Dinger and Zheng) used to calculate the densest packing. Modified Andreassen equation is given by

CPFT = {(d – do)/(D – do ) }q 100

CPFT = Cumulative percentage finer than (by volume); d= Particle size; d0 = Minimum particle size; D = Maximum particle size; q = Distribution coefficient or exponent

The value of exponent (q) depends on the workability requirement of concrete and it varies between 0.21 and 0.37. The value of q is directly proportional to coarse aggregate and inversely proportional to the fine aggregate quantity.

Rosin-Rammler Model

The various ingredients of concrete have different characteristic diameters, which have been denoted by D’ in the equation adopted in the Rosin-Rammler model.

where, R(D)=Residue fraction (percentage fraction); D= Diameter, D’= Characteristic Diameter, N= Constant varying b/w 1.04 – 4 (usually lies in 1 – 2 range)

Figure 12. Ideal grading curve for q = 0.27 and actual overall particle size distribution [23]


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This equation was also used by Goltermann et al. [24] in discrete approach for finding the characteristic diameter required for calculating the packing density of mixture.

INFLUENCE OF PARTICLE PACKING ON PROPERTIES OF CONCRETE COMPRESSIVE STRENGTHGopinath et al (2011) investigated for the mix design of M30 and M40 grade concrete using particle packing model (PPM) and EMMA analytical tool [30]. It was observed that the compressive strength approached close to design compressive strength using PPM with minimum cement content compared to concrete designed using standard design procedure in a similar manner. Santhanam et al (2003) stated that the compressive strength of cement mortar mix using PPM was found to be increased by 28-30% when compared to the mix prepared according to normal procedure (IS : 516 - 1959 , Reaffirmed 2004 ) [23], as represented in Figure 12.

Rizwan et al. stated that aggregate phase packing plays a major role influencing the properties of high performance self-consolidating mortar [31]. The distribution coefficients were varied from 0.2 to 0.5 in the modified Andreassen model and gradation curves were obtained. It was observed that voids are decreased at a distribution coefficient of 0.35 and hence, obtained high compressive strength. Razak et al (2013) [32] studied the mechanical properties of self-consolidated concrete mixed with varying percentages of by-product “Palm oil clinker”, the mix design prepared using particle packing concept. It was observed that POC 0 with high paste volume produces high compressive strength SCC when compared to all other combinations. In addition to dilution effect, significant influence on strength was observed due to particle packing.

Fennis (2011) [33]explained about the different mixture compositions on strength parameters of concrete and used a cyclic design procedure. It was observed that the cement spacing factor in concrete, with or without adding supplementary cementitious materials, would influence the packing densities thereby changing the water demand and affecting compressive strength of concrete. Yu et al (2015) [34] developed a method to determine the optimum mix proportions of binders and fibres in Ultra high performance fibre reinforced concrete (UHPFRC) using the Andreassen particle packing model. High compressive strength was observed with binder content of about 620 kg/m3 and fibre volume content of 2% among various combinations. In addition, among various available fibres, short straight fibres were found to be more effective for high compressive strength.

PACKING DENSITYFennis et al (2011) [33] suggested a cyclic design method for design mix based on particle packing for cement pastes mixed with quartz powder. It was observed an increment of 15% in compressive strength when 20% of cement is replaced by Quartz powder. It is imperative to note that the enhancement in strength is due to particle packing/filler effect as quartz does has no reactive silica. Effect of particle packing is shown in Figure 13.

Klein et al. [36] studied about the behaviour of concrete prepared by particle packing of cement and silica fume. Moreover, optimum content of silica fume was found to be 15% of cement weight using compressive packing model (CPM) to attain maximum packing density and is represented by Figure 14. The study on relation of packing density with aggregate PSD (coefficient of uniformity Cu) was carried out by Santhanam et al. [37] and found that an

Figure 13. Particle packing curves of cement with quartz powder [35]

Figure 14. Variation of packing density of cement - silica fume mix with cement packing model [36]


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increase in particle density improves the flow properties of concrete.

Relationship between packing density and Cu is depicted in Figure 15. Chen et al. (2012) [38] studied the effect of ultrafine cement on packing density, workability, and strength. An addition of 20% ultrafine cement enhanced the properties of concrete significantly. The results of study on packing density and void ratio are given in Figure 16. Similarly, fly ash microsphere (FAM) was added to the cement mortar to improve the packing density by Kwan et al. [39]. Both dry and wet packing methods were applied to concrete mixes by Kwan et al. in another study [40].

Vibration is found to be more effective than tamping in case of wet packing and the addition of SP reduced the voids by 39% as it directly reduces water content in the mix. Elrahman et al (2013) studied the influence of fly ash, silica fume on the performance of high performance concrete [41]. Fuller model was used for achieving best packing density. It was found that the replacement of cement in high performance concrete based on particle packing model increased the mechanical properties and durability characteristics.

Fennis et al. tested more than 100 cement pastes design by particle packing using various cementitious materials and suggested sustainable concrete mix with lower cement content of about 110 kg/m3 [33]. It was observed that along with a remarkable reduction of Portland cement content in concrete by 57%, their compressive strength and modulus of elasticity improved by about 23% and 7%, when compared to conventional concrete. Smit et al. studied the effect of paste content on the properties of high strength concrete. Two sets of concrete was tested with varying percentages of paste content and super plasticizer and it was observed that the modulus of elasticity decreases with increase in paste content varying from 25-60% [42].

SHRINKAGE AND CREEPFennis et al. experimented to determine the environmental friendly concrete mix with reduced cement content based on particle packing [33]. Shrinkage and creep effects were found to be less in the concrete mixes designed using PPM compared to other concrete mixes based on codal provisions. This has been shown in Figure 17. Bentz et al. studied properties of cement based materials for varying particle

Figure 15. Relationship between Cu and particle packing density [37]

Figure 16. Variation in packing density and void ratio with increasing SFC Content [38]

Figure 17. (a) Shrinkage of mixtures designed by PPM and Eurocode 2 (b) Creep of mixtures designed by PPM and Eurocode 2 [33]


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size distribution of cement as well as water cement ratio[44]. It was observed that the coarser cement particle with lower water-cement ratio yield better results in terms of chemical shrinkage. Soliman et al tested on the two types of concrete mixes prepared by adding partially hydrated cementitious materials (PHCM) and superabsorbent polymer (SAP) designed by particle packing concept [45]. It was observed that PHCM would reduce the early age shrinkage of ultra-high performance concrete more effectively.

Gokce et al (2016) developed significant relationships between the durability performance parameters such as water penetration resistance, resistivity, chloride migration coefficient and abrasion resistance with references to fines content at different water-cement ratio [43]. It was observed that the fines had more impact on these durability parameters in case of concrete specimens with high water-cement ratio rather than low water-cement ratio concrete specimens. The results of the study are shown in Figure 18. Ranjbar et al (2016) incorporated palm oil fuel ash in self-compacting concrete and found an improvement in acid and sulphate resistance [46]. In addition, reduction in surface water absorption was found while maintaining the same strength.

Figure 18. Impact of fines content on durability properties of Slag cement concrete [43]

Figure 19. Loose Packing Density as a function of a) Facet Number b) Sphericity [47]


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EFFECT OF PARTICLE PACKING ON AGGREGATES

Stroeven et al. evaluated the effect of grain shape on packing of aggregate by using 2D and 3D simulations by versatile DEM (Digital Elevation model) system “HADES’ [47]. As the facet number and Sphericity increases, the packing density also increases as seen in Figure 19 and Figure 20. Figure 21 shows the various shapes used to study the random packing density.

Shen et al (2010) [48] derived a two-step procedure for evaluating the impact of particle size and shape distributions characteristics on aggregate structure by particle packing theory in case of HMA mixtures. Using DEM simulation, a gradation parameter, fs was determined which links the aggregate packing to volumetric properties. Moreover, aggregate interlocking and contact forces are dependent on particle size as shown in Figure 22. It is derived that every particle greater than 2.36 mm contribute to stress transfer through strong force chains while size less than 1.18 mm would increase the number of weak links, which does not take part in transmission of stresses.

Figure 21. Mono-sized random packing with different particle shapes: (a) Tetrahedron (b) Hexahedron (c) Octahedron and (d) Sphere [47]

Figure 22. Aggregate contact and interlock in DEM simulation using ball-hiding technique [48]

Figure 20. Random Packing Density as a function of a) Facet Number and b) Sphericity [47]


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Figure 23. Comparison of normal and compacted packing density of different binary mixes & Comparison of three parameters with de-Larrard [50]


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Mohammed et al (2012) investigated a comparison of theoretical packing models such as 4C and Toufar with the experimental results to test the packing degree of natural and crushed aggregate [49]. Moreover, particle packing of natural aggregate was found to be better than that of crushed aggregate types due to its irregular shape i.e., Sphericity and roundness.

Kwan et al. [50] carried out an experimental study on the packing density of compacted and uncompacted binary mixes of angular rock aggregate particles and calculated the interaction functions of the loosening, wall and wedging effects, as shown in Figure 23. These experimental results were validated by the results obtained by Larrard and other authors.

Chen et al. studied the effects of ellipsoidal aggregate’s particle size distribution (EPSD), shape and volume fraction (Vf) on wall effect of concrete using serial sectioning analysis technique [51]. A microstructure model was simulated using random sequential packing of polydispersed ellipsoidal aggregate particles with the modified Fuller distribution and Equal Volume Fraction (EVF) distribution and the result is shown in Figure 24. It was observed that the dependency of its thickness on the three gradients is less and its thickness is equivalent to maximum equivalent radius (Deq/2) of ellipsoidal particles. In addition, the influence of particle shape and EPSD on volume of solid phase is less. Bond strength of concrete was found to be less in case of fuller distribution.

Figure 24. Statistical results of VV, SV and l -3 for the Fuller and EVF distribution [51]

Figure 25. a) Sequential packing model and b) Particles suspension model [55]


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Reisi et al. used PFC 3D software for calculating the packing density of aggregates in view of shape and grading of aggregates through simulation [52]. Dynamic motion of aggregates and interaction of spherical particles were modelled through DEM. Packing density of different aggregates was determined using binary mixes and observed that the results were in match with the experimental results. Stroeven et al. provides a methodological approach for shape assessment of two types of aggregates namely, fluvial gravel and crushed rock by means of stereological and Fourier series [53]. The stereological analysis derives 3D shape information from 2D particle projection profiles whereas Fourier analysis provides more efficient shape parameters like circularity, roundness, sphericity etc. when compared to conventional 2D global shape parameters. It helps us in distinguishing different types of aggregates and thereby, providing effective aggregates for concrete production. Fluvial gravel was found to be more uniform in shape as crushed rock particles cover a wider range of shape indices. He et al. characterized the PSD’s and shape parameters of two different fine aggregate i.e., River sand (RS) and Crushed Rock (CR) by different experimental methods [54]. Experimental results showed lesser fine particles in case of RS than CR through images obtained by optical microscopy technique.

Roughness and angularity is higher in CR particles. Dynamic image analysis method (Flow cell) provides underestimated PSD due to missing of large particle by sedimentation while laser diffraction method provided overestimated PSD of upper bound due to its sensitivity to geometrical and optical properties of particles. Static image analysis was found to be a reliable and accurate and shows a larger proportion of fine particles in CR when compared to RS. In case of shape parameter analysis, both are found to have same dimensional ratio.

Sobolev et al. developed simulation algorithms for modelling of dense packing of large sized aggregate system in concrete using Sequential Packing Model (SPM) and Particle Suspension Model (PSM) [55].

In case of SPM, the new spherical particles are glued to each other with no spacing between them while the particles are having a minimal spacing between each pair of particles for perfect mixing as shown in Figure 25. Brouwers et al. worked on the Chinese method of composing SCC i.e., packing of aggregates (sand and gravel) followed by filling of paste in voids of aggregates where optimization of PSD was given more consideration [56]. Three different mixes consisting of three types of sand (0-1 mm, 0-2 mm & 0-4 mm), gravel (4-16mm), slag blended cement and a super plasticizer

(polycarboxylic ether) were analysed using Andreassen model. Mixes that adopted Andreassen model exhibited better resistance to segregation and workability.

CONCLUSIONSophisticated analytical tools for determining the particle size and particle size distribution (PSD) are discussed. It was observed that Scanning Mobility Particle Sizer and Nanoparticle tracking analysis provide highly accurate and efficient results compared with techniques. In order to determine the optimum packing density, particle-packing models have been proposed in previous research studies. Review on these models and its application in concrete mix proportioning by formulating the packing density are presented. Present mix proportioning emphasizes on sieve analysis of aggregates and that does not optimize the packing density. Use of particle-packing concept and relevant specifications are suggested to consider in the respective codal provisions to achieve economical and sustainable design of concrete.

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POINT OF VIEW

Athira Gopinath holds a Master’s degree in Geotechnical Engineering from National Institute of Technology, Warangal. She is a doctoral research scholar in Civil Engineering Department, BITS Pilani, Hyderabad. She is currently working on characterization of supplementary cementitious materials based on agro-industry wastes. Her research interests include mechanical and durability properties of blended cement concrete.

Akilesh Ramesh holds a Master’s degree in Structural Engineering and Bachelor’s degree in Civil engineering from National Institute of Technology Calicut, Kerala, India. His area of interests includes bridge engineering and design, earthquake resistance and ductility behaviour of structures, performance improvement of concrete mixes, durable and eco-friendly concrete structures and related fields.

T. Naveen Kumar is an Assistant Engineer at Irrigation and C.A.D Department, Government of Telangana. His research interests are mainly material characterization, alternative materials and special concrete.

Dr. Bahurudeen A. holds a PhD from Indian Institute of Technology (IIT) Madras, Chennai. He is an Assistant Professor at Birla Institute of Technology and Science Pilani – Hyderabad Campus (BITS Pilani). His research interest includes supplementary cementing materials, characterization and durability of concrete structures.

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