4.7 Article

Influence of marble waste as partial replacement of fine aggregates on strength and drying shrinkage of concrete

Journal

CONSTRUCTION AND BUILDING MATERIALS
Volume 228, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2019.116730

Keywords

Waste marble; Drying shrinkage; Compressive strength; Microstructure

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In the present study, the influence of using marble waste as fine aggregates in concrete is investigated. The marble waste is used as partial replacement of natural river sand, with the replacement levels varying from 10 to 60%. The effect of using marble waste aggregates is investigated in terms of workability, compressive strength, drying shrinkage and micro-structural properties of concrete. Test results indicate that marble waste aggregates can be incorporated into concrete to improve its strength and shrinkage properties. There is an improvement in compressive strength by 20% with the incorporation of marble waste aggregates, with the corresponding decrease in drying shrinkage by 30%. Maximum benefit in terms of compressive strength and drying shrinkage was observed till 40% replacement level. Microstructural analysis also revealed densification of concrete matrix, which is attributed to refinement of pores due to physical and chemical changes in the concrete matrix. A comparison of observed shrinkage strains with the predicted values obtained from the well-established prediction models confirm the requirement for incorporation of an additional parameter based on the percentage of marble waste to be adopted to predict the shrinkage values of such mixes accurately. Further, a multivariable regression model is developed for the prediction of shrinkage strain of mixes containing marble waste aggregates. The various parameters included in the prediction model are 28-day compressive strength, drying duration and proportion of marble waste used as fine aggregates. A high correlation coefficient (R-2) obtained between the experimental and predicted values indicate the effectiveness of the proposed model. (C) 2019 Elsevier Ltd. All rights reserved.

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