Influence Of Aggregate Size Research Articles

Pore structure evolution of mortars with manufactured sand aggregate under the influence of aggregate size and water saturation environment

To investigate the effect of aggregate size on pore structure and pore evolution in water saturation of manufactured sand aggregate (MSA) mortars, five MSA mortars with small range of aggregate sizes were prepared and their pore characterization were monitored over 365 days. The findings showed that an increased surface volume ratio (SVR) leads to an increase in total and macroporous porosity, and three pores with different connectivity were quantified using double T2 cutoff method. Moreover, continued hydration by unhydrated cement caused an increase in micropores during the initial stage of service in a water-saturated environment for 365 days, and an increase in macropores at the later stage as a result of enhanced hydraulic solvation, furthermore, solvation was more significant in the mortar with larger SVR to the point where it eventually produced pores larger than 100 μm. This research would provide guidance for the durability evaluation of cement mortars.

Influence of aggregate size on pervious concrete properties with and without construction and demolition waste

This research evaluates the impact of aggregate sizes on pervious concrete properties, comparing aggregates of 12.5 mm and 19 mm, as well as replacing natural aggregates with recycled aggregates set at 0% and 50%. Four types of pervious concrete were produced, and their properties were determined: density, porosity, permeability coefficient, compressive strength, flexural tensile strength, and abrasion resistance. The results indicate that water permeability is directly related to pore size and is influenced by aggregate size (90% of the variation in pervious concrete permeability) and, to a lesser extent, by recycled aggregate content (10% of the variation). Mixes with larger aggregates (19 mm) demonstrated higher permeability coefficients. Replacing natural aggregates with recycled aggregate did not significantly affect the mechanical properties of pervious concrete, highlighting the effectiveness of waste processing and mixing procedures, allowing for the incorporation of 50% of recycled aggregate. The concretes met the requirements of the American Concrete Institute, suggesting technically feasible conditions for sustainable practices in the construction industry.

Influence of aggregate size on relative velocity change of high-frequency ultrasound in progress of concrete curing

Previous research revealed that wave interferometry can be more effective than existing ultrasonic techniques in monitoring the strength development of early-age concrete. To improve its accuracy, this study aims to examine the impact of aggregate size on the relative velocity change of high-frequency ultrasound during the concrete curing process. Concrete cubic specimens with 150 mm sides were prepared using two different maximum aggregate sizes, 4.75 mm and 20 mm. These specimens were subjected to water curing for the first 28 days, followed by air curing until 150 days. Ultrasonic tests employing a 1 MHz frequency were performed throughout the curing process, and the measured signals were analyzed by applying wave interferometry techniques. The test results suggest that the influence of aggregate size was evident in both the initial water curing and the subsequent air curing. During the initial curing, the specimen with larger aggregates exhibited a greater increase in relative velocity change. Thereafter, the velocity change decreased during the subsequent air curing in both specimens. Based on the results, the correlation between relative compressive strength and relative velocity change was proposed considering the maximum aggregate size. The findings offer insights into the use of ultrasonic methods to evaluate the characteristics of early-age concrete.

Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters

This study investigates the influence of aggregate size, aggregate-to-cement ratio, and compaction effort on pervious concrete's porosity and compressive strength. It proposes using response surface methodology and machine learning techniques to predict porosity and compressive strength. Fifteen mix designs (three aggregate sizes and five aggregate-to-cement ratios) with seven compaction energy levels were employed. Experimental data analysis using various regression models revealed that the quadratic model provided the best fit for predicting both porosity and compressive strength. Machine learning models were employed to predict porosity and compressive strength more accurately. Among the models investigated, the artificial neural network achieved superior performance across all datasets (training, testing, and validation). This suggests the artificial neural network model can effectively capture the complex relationships between input and response variables. Sensitivity analysis using SHAP (SHapley Additive exPlanations) revealed that compaction energy significantly impacts both porosity and compressive, while aggregate size has the most negligible influence.

Evaluation of soft computing techniques for predicting Marshall Stability of waste plastic-reinforced asphalt concrete

ABSTRACT Pavement engineering has long prioritized enhancing the quality and durability of asphalt concrete, a foundational material in road construction. This study employs advanced soft computing techniques to predict the Marshall Stability (MS) of asphalt concrete reinforced with waste plastic. Techniques such as Artificial Neural Network (ANN), Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), and Bagging RT are utilized. Evaluation of model effectiveness is conducted using seven statistical metrics: coefficient of correlation (CC), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), scatter index (SI), and comprehensive measure (COM). Among the applied models, the Bagging RT model emerges as the top performer, exhibiting superior performance across multiple metrics. Specifically, the Bagging RT model achieves impressive CC values of 0.921 and 0.834, indicating strong correlations between predicted and actual MS values. Additionally, it demonstrates low error metrics, with RMSE values of 1.632 and 2.869, and MAE values of 1.207 and 2.081, respectively. A sensitivity analysis conducted for the Bagging RT-based model underscores the significant influence of aggregate size on MS prediction, highlighting the model’s capability to elucidate critical factors shaping material stability.

A Study on the Influencing Factors of the Compressive Strength of Planting Concrete

It is a study on the influence of aggregate size, water-cement ratio, and target porosity on the compressive strength of planting porous concrete. Experiment results show that porous concrete’s compressive strength is negatively correlated with target porosity and aggregate size; no distinct linear relation with the water-cement ratio is indicated, and there is an optimal water-cement ratio in different mix proportions. Among the three factors influencing the compressive strength of porous concrete, target porosity ranks first in influence and is followed successively by aggregate size and water-cement ratio. Besides, the paste-coating method is helpful in the formation of connected pores of porous concrete.

Rock Aggregate Size Influence on Physical Degradation of Laminate Sedimentary Rocks in Flexible Pavement

Rock aggregates are the most fundamental material of highways, railroads, and other construction activities. Degradation of aggregate is the disintegration of aggregate due to the wheel load and moisture saturation causing loss of support in unbound granular layer and reduction of drainage in pavement materials. Sedimentary rocks from different quarries were evaluated to determine the influence of aggregate size on the degradation of materials. This study is to find out the effect of size on physical parameters of aggregate used in flexible and rigid pavement. The performance of river rock and crushed rock physical properties has been tested and compared. In order to determine the effect of aggregate size on their degradation properties, different sieve ranges were selected. The sieve sizes are graded into four categories according to Indian Standard sieve: R1 (20 mm-16 mm), R2 (16 mm-12.5 mm), R3 (12.5 mm-10 mm), and R4 (10 mm-4.75 mm) for degradation test such as, aggregate impact value (AIV), aggregate crushing value (ACV), water absorption (WA) and weathering test such as soundness and slake durability. It is observed that the larger size of aggregate has given better resistance against impact load, abrasion, weathering action and water absorption compare to smaller size over the same volume of rock. The rate at which aggregate degradation occurs is influenced by size. Along with the wheel load, environmental factors including moisture and temperature changes are the primary causes of the early degradation of aggregate. The river rock has a better resistance against degradation and weathering action as it has lesser lamination (a small scale sequence of fine rock layer). Adoption of larger size aggregate in the pavement base course is suggested to withstand the effect of environmental action and wheel load, especially on the friable and quickly disintegrated rock. Arating value, rating index and regression equation have been developed for aggregate degradation. Keywords: Particle Size, Properties of Rock, Weathering, Slake Durability, Degradation Index

Effect of aggregate size on water distribution and pore fractal characteristics during hydration of cement mortar based on low-field NMR technology

The influence of aggregate size (AS) on the hydration of cement mortar is very important for improving the quality and stability of cement mortar. This study aims to study the effect of AS on water distribution and pore structure characteristics on the early-age hydration of cement mortar by low-field NMR technology. The hydration degree and fractal dimension of the hydration process were calculated by the T2 spectrum. The water distribution models at different times and locations during the hydration process were established based on the MRI bitmap data. The variation law of water volume at different locations was studied. The results indicate that: In the process of hydration, some of the water is consumed by the hydration reaction, and some of the water oozes out. The water in the large pores flows to the small pores under the action of hydrostatic pressure. AS affected the uniformity of the cement mortar at the initial state, and the gradation of the sand could improve the uniformity. The smaller AS was, the more complex the pore water distribution and changed during hydration. The bleeding mass was directly proportional to the porosity and inversely proportional to the specific surface area of particles. The effect of porosity on bleeding mass was more significant than AS. Pore fractal dimension was negatively correlated with aggregate diameter. In the initial stage, when the aggregate composition became complex, the change of fractal dimension with time also became complex. The fractal dimension increased gradually with the increase of hydration time. The gel pores (G) did not have fractal characteristics from beginning to end, capillary pores (C) and transition pores (T) had fractal characteristics after a particular time of hydration reaction. Although the air-voids (A) had fractal characteristics from beginning to end, they were not continuous.

An experimental study and axial tensile constitutive model of the toughness of PP-SACC for rapid repairs

To improve the economic benefits of engineered cementitious composites and control the repair cycle, repair materials were designed, with the key components of the mixture being low-cost polypropylene (PP) fibers and fast-setting sulfoaluminate cement. The effects of water/binder ratio, fiber content, and aggregate particle size on the flowability, mechanical properties, and toughness of the polypropylene fiber-reinforced sulfoaluminate cementitious composite (PP-SACC) were explored. Based on experimentally measured axial tensile stress–strain curves, a constitutive model of PP-SACC was derived in terms of fiber content and water/binder ratio. Additionally, the correlation coefficients representing the relationships of the mixture indices with the tensile properties were explored based on revised gray relational analysis. Test results indicated that fiber content and water/binder ratio were the most important factors affecting the mechanical properties, toughness, and fluidity of the material; in contrast, the influence of aggregate size was slight. The PP-SACC mixture with an aggregate size of 75 µm, a water/binder ratio of 0.30, and a fiber content of 3.0% demonstrated an excellent degree of toughness and exhibited a flexural hardening phenomenon under bending load.

Experimental study on size of aggregates, size and shape of specimens on strength characteristics of pervious concrete

Pervious concrete has a wide range of applications including pervious pavements, rigid drainage layers and water-free floor. In this paper, the influence of aggregate size, shape, and size of specimens on the mechanical properties of pervious concrete has been investigated. Tests on pervious concrete include direct compression on cubes and cylinders, splitting tension, and flexural tests on respective cylinders and prisms were carried out on three different mixes with aggregate sizes of 10, 12.5, and 20 mm. The height of specimens was kept as the primary variable whereas the aspect ratio was kept constant. Experimental results indicated that when aggregate size and height of specimen increases, compressive, splitting tensile and flexural strength decreases. Using Size effect models such as Linear model, Bažant's Size Effect Law and Modified Size Effect law, an approach is proposed to predict non-standard specimen strength from known standard specimen strength with high accuracy.

Influence of Aggregate Size and Shape on the Compressive Strength of Concrete

This paper investigates the effect of the size and shape of coarse aggregates on the compressive strength of concrete. Concrete strength is affected by the surface texture, grading and maximum aggregate size. Six different sizes of coarse aggregate have been selected for both angular and rounded coarse aggregate. The coarse aggregates were used in the production of concrete and tested for workability, density and compressive strength. The specimen was cured for 3, 7, 14, 21 and 28 days by full water immersion. The results indicated that under the same curing conditions and water-cement ratio, the compressive strength of concrete produced with both angular and rounded aggregates increased with increasing aggregate size, up to an aggregate size of 14 mm. The optimum compressive strength of 27.58 N/mm2 and 25.88 N/mm2 were achieved at 28 days curing and 14 mm aggregate size for concrete with angular and rounded aggregates respectively. Coarse aggregates with angular shape result in concretes with better compressive strength than coarse aggregates with a rounded shape. The model equation developed to predict the compressive strength of rounded aggregate has R2 value of 95.66%, and the higher the value of R2, the better the model fits the data.

Short-term Study of the Influence of Aggregate Size on the Fire Resistance of Concrete

This study aims to investigate the effect of aggregate size on the fire resistance of concrete. The binder for this investigation was 42.5R CEM II/A-L Portland limestone cement, and the crushed granite sizes were 20mm, 12.5mm, and a blend (20 mm + 12.5 mm). The concrete was designed with a mix ratio of 1:2:4 (batching by weight) and 0.55w/c. The workability of the concrete was determined using the slump method. The concrete specimens were cured for 7, 14, and 28 days by immersion and later exposed to heat temperatures of 26.8 °C (Room temperature), 60 °C, 120 °C, 180 °C, and 240 °C. The compressive strength of normal concrete (without heating) had the optimum with concrete produced with blended aggregate size and conversely the least with 20mm aggregate size concrete. When subjected to heat, the optimal performance noted for the influence of aggregate size on fire resistance was also with concrete produced with combined blend (20 mm + 12.5 mm) sizes. The p-value (Prob > F) for the whole model test is less than 0.05, indicating that there is a significant relationship between the grain size, temperature, and strength loss. The use of blended sizes of 12.5 mm and 20 mm is therefore recommended for concrete works and especially when fire resistance is a requirement.

Análise numérica da influência do tamanho do agregado no dano mecânico do concreto sob altas temperaturas

Abstract The influence of aggregate size on the degradation process of material exposed to high temperatures is not a consensus among the scientific community because changes in the microstructure impact the macrostructural performance. To contribute to this investigation this work presents a thermomechanical model to evaluate aggregate size influence on the concrete mechanical damage under high temperatures. The material is considered as two-phase - aggregate and matrix - and three-phase - in which the interfacial transition zone is added. Concerning geometries, models in 2D and 3D are simulated. A finite element software is used with a weak coupling strategy that reduces the computational cost, and a user subroutine is implemented to define the constitutive model. The results show that the aggregate size influences both the average damage and the damage distribution along the synthetic specimen.

Numerical Investigation of Aggregates Size and Volume Fraction on Gas Permeability of 3D Meso-Scale Concrete

In this paper, the influence of aggregate size and volume fraction on the permeability properties of meso-scale concrete is investigated. Three volume fractions of aggregates are investigated, varying from 10, 20 and 30 %. Furthermore, three aggregate sizes are examined, 10, 14, and 16 mm. In this work, numerical results provide some indications: increasing volume fraction leads to increased fracture energy, tensile stress, and crack opening. The results show that increasing crack width and decreasing volume fraction lead to an increase in gas permeability.

Influence of aggregate size on shear mechanism of reinforced concrete beam subjected to impact load

This study presents the influence of aggregate interlock, in terms of aggregate size, on shear mechanism of reinforced concrete beam under a drop-weight impact using a non-linear finite element model. Four aggregate sizes (8-, 16-, 24-, and 32-mm) were used to study the variation of the shear force at the critical section. Large shear force was recorded at the critical section for the specimens with the 32-mm aggregate size regardless of the impact mass and velocity. This is expected since larger aggregates create rougher surfaces and transfer larger shear forces than smaller ones. As the impact mass increased from 868 to 1700 kg, the critical section shear force decreased on average by 5.3%, regardless of aggregate size. Increasing the impact velocity by 41% decreased the critical section shear force by 58%, 56%, 45%, and 41.5% for the 8 mm, 16 mm, 24 mm, and 32 mm aggregate size specimens, respectively. The influence of aggregate size on the shear transfer mechanism was also studied in the presence of shear reinforcement and increased span-to-depth ratio. The difference in shear force at the critical section among the specimens with the different aggregate sizes is negligible in the presence of shear reinforcement indicating that the presence of stirrups reduced the contribution of aggregate interlock in shear resistance. Increasing the span length by 67% (from 3 to 5 m) decreased the effect of aggregate size by 95% on average.

A Mesoscopic Model for Predicting the Chloride Diffusivity’ Representative Volume Element in Concrete as a Random Composite

Chloride diffusivity is the most crucial factor for evaluating durability and predicting service life of concrete structures exposed to chloride environment. In the present paper, concrete is considered as a random heterogeneous composite of three phases: the aggregates, the matrix, and the interfacial transition zones (ITZ) between them. A mesoscopic model has been established based on the random aggregate model and a numerical model for calculating the chloride diffusivity has been proposed. The influences of aggregate size and the properties of the interfacial transition zones on chloride diffusivity of concrete are also discussed. The chloride diffusivity is size dependent because aggregates are randomly distributed in concrete. The chloride diffusivity under different sizes is analyzed by numerous numerical simulations, and the representative volume element (RVE) of chloride penetration into concrete is researched by the statistical method.

Investigation on influence of size of aggregate and effect of strength on altering the ratio in low nominal concrete mixes

Concrete is rapidly changing because concrete for the 21st century must be stronger, more durable, more constructible, more environmentally friendly, more practicable and more cost-effective. The main aim of this research work was to investigate the influence of aggregate size and their effect on strength by altering the available nominal concrete mix ratios. And also this research studies would give consciousness among researchers, engineers and the public about increase in strength while altering the conventional mix of M5 and M 7.5 concrete grades. This investigational study was carried out on 7.5 and M5 concrete grades, which are used for masonry wall foundations and concrete bases with the lean mix. For identifying the effect of strength, concrete prepared with M5 grade conventional mix (1:5:10) to 1:5:9 & 1:5:8 and M7.5 grade conventional mix (1:4:8) to 1:4:7 & 1:4:6 along with 40 mm downgraded coarse aggregate and 53 grade cement. The relative clause of these above concrete mixes was studied. And the effect of using 40% of 20 mm size aggregate and 60% of 40 mm aggregate for the above nominal concrete mixes were also studied. Concrete mix with ratio 1:5:8 was stronger than the 1:5:10 mix by about 3.5 times and concrete mix with ratio 1:4:6 was stronger than the 1:4:8 mix by about 5 times. The relative cost was increased for the above two mixes by 2.56% and 4.05% respectively.

Freeze–thaw and thermal cycle durability of pervious concrete with different aggregate sizes and water–cement ratios

ABSTRACT Use of pervious concrete (PC) for permeable pavements can help avoid urban waterlogging and urban heat islands. However, PC typically has low strength and poor durability owing to its special porous structure. The purpose of this study was to understand the influence of aggregate size and water–cement ratio (w/c) on PC under different environmental conditions (freeze–thaw and thermal cycles). The experiment designed a PC made with five aggregate sizes (3–5 mm, 5–7 mm, 7–9 mm, 9–11 mm, 11–13 mm) and five w/c values (0.21, 0.25, 0.29, 0.33, 0.37). The mass loss, compressive strength and life of all mixtures were tested under freeze–thaw and thermal cycles. Results indicated that aggregate size and w/c were identified as important factors determining the properties of PC and were evaluated using survival analysis to determine the optimal mixture. As the number of freeze–thaw cycles increased, mass loss and risk of damage increased while compressive strength decreased. As the number of thermal cycles increased, mass loss increased and compressive strength first increased and then decreased. After thermal cycling, high w/c had better frost resistance, contrary to the results for direct freezing–thawing.

Properties of permeable ceramic brick prepared with felsite tailing

Using tailings to prepare permeable brick is a promising approach to solve the existing problems of tailing being hugely generated. In this paper, the felsite tailing with size of 0.3–4.75 mm is used to prepare the permeable ceramic brick, and the influence of aggregate size on porosity, permeability, mechanical properties and frost resistance are investigated. The coarser aggregate could increase the porosity, pore retention efficiency and permeability of permeable brick. In addition to porosity, the strength of permeable brick is also related to the aggregate gradation. The permeable bricks with four-grade aggregate generally have higher compressive and flexural strength than that with single- and two-grade aggregate, and using four-grade aggregate could prepare a permeable brick with both high permeability and higher strength. More importantly, the ratio of flexural to compressive strength of permeable ceramic brick is about 0.65, which is obviously higher than that of ordinary pervious concrete. Under the freezing-thawing environment, the mass loss of permeable brick is very low, and the increase of finer aggregates could restrain the strength loss of permeable brick.

Modeling of heterogeneous materials at high strain rates with machine learning algorithms trained by finite element simulations

Great progress has been made in the dynamic mechanical properties of concrete which is usually assumed to be homogenous. In fact, concrete is a typical heterogeneous material, and the meso-scale structure with aggregates has a significant effect on its macroscopic mechanical properties of concrete. In this paper, concrete is regarded as a two-phase composite material, that is, a combination of aggregate inclusion and mortar matrix. To create the finite element (FE) models, the Monte Carlo method is used to place the aggregates as random inclusions into the mortar matrix of the cylindrical specimens. To validate the numerical simulations of such an inclusion-matrix model at high strain rates, the comparisons with experimental results using the split Hopkinson pressure bar are made and good agreement is achieved in terms of dynamic increasing factor. By performing more extensive FE predictions, the influences of aggregate size and content on the macroscopic dynamic properties (i.e., peak dynamic strength) of concrete materials subjected to high strain rates are further investigated based on the back-propagation (BP) artificial neural network method. It is found that the particle size of aggregate has little effect on the dynamic mechanical properties of concrete but the peak dynamic strength of concrete increases obviously with the content increase of aggregate. After detailed comparisons with FE simulations, machine learning predictions based on the BP algorithm show good applicability for predicting dynamic mechanical strength of concrete with different aggregate sizes and contents. Instead of FE analysis with complicated meso-scale aggregate pre-processing, time-consuming simulation and laborious post-processing, machine learning predictions reproduce the stress–strain curves of concrete materials under high strain rates and thus the constitutive behavior can be efficiently predicted.