Effect of coarse aggregate blending on short-term mechanical properties of self compacting concrete

Jawahar, J. Guru; Sashidhar, C.; Reddy, I. Neelakanta; Peter, J. Annie

doi:10.1016/j.matdes.2012.06.063

Cited by 28 publications

(9 citation statements)

References 16 publications

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“…Self-compacting concrete is defined as a cementitious material that can flow under its own weight and was first developed in the late 1990s in Japan. SCC deforms efficiently and shows maximum resistance to segregation and bleeding as per American Concrete Institute committee 237 R-07 [ 3 ]. Moreover, due to its workability, SCC is more often used where there is a need of creating different shapes of the elements or there are some parts of elements hardly reachable [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash

et al. 2021

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Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash

et al. 2021

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“…These aggregates may be of different types (siliceous, plastic, calcareous and even ceramic [8]) and having different textures (rough or smooth) [9] as a result having different levels of bonding to the mortar [10], and have an angular and elongated or rounded and compact shape, all of which can have a significant influence on the stress distribution within the concrete material [8,11] and thus contribute to differences in mechanical and transport behavior of the final formed composite. In addition, the aggregate volume fraction and gradation have an effect on the mechanical performance [12,13].…”

Section: Introductionmentioning

confidence: 99%

Towards a realistic morphological model for the meso-scale mechanical and transport behavior of cementitious composites

Niknezhad

Raghavan

Bernard

et al. 2015

Composites Part B: Engineering

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“…e test results showed that the larger the maximum size of the coarse aggregate, the greater the heterogeneity of the specimens. Jawahar et al mixed coarse aggregates of different sizes and found that the mixture had little effect on the compressive strength of the self-compacting concrete [19]. With the increase of the coarse aggregate content, the splitting tensile strength of concrete increased.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Microcracks of Fly Ash/Slag Concrete with Cobble as Coarse Aggregate

Song-fang

Gao

2022

Advances in Materials Science and Engineering

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In order to improve mechanical properties of fly ash/slag concrete with large size cobble as coarse aggregate, this paper analyzes the effect of different factors on the concrete through the flexural strength test. The Monte Carlo simulation is used in the finite element solver of ANSYS to conduct the four-point bending beam test. Three-dimensional and two-dimensional finite element models are established to discuss how the gradation of large size cobbles affects the performance of the concrete by comparing macromechanical experiments. Results show that the gradation of large size cobbles is the main factor affecting the performance of the concrete. Slag generates the least effect on the concrete with cobble as coarse aggregate. When the mixing amount of slag and fly ash is 10%, the concrete presents the best flexural performance. Through the numerical loading test of the two-dimensional model for fly ash/slag concrete with cobble as coarse aggregate, it can be concluded that the change of the concrete follows the law of macromechanical properties.

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Effect of coarse aggregate blending on short-term mechanical properties of self compacting concrete

Cited by 28 publications

References 16 publications

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash

Towards a realistic morphological model for the meso-scale mechanical and transport behavior of cementitious composites

Numerical Analysis of Microcracks of Fly Ash/Slag Concrete with Cobble as Coarse Aggregate

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