Optimizing of Self-Compacting Concrete (SCC): Synergistic Impact of Marble and Limestone Powders—A Technical and Statistical Analysis
The disposal and recycling of industrial by-products such as marble and limestone powders pose pressing environmental challenges due to the substantial amounts of waste generated annually by marble processing plants and limestone quarries. The integration of these by-products into concrete productio...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/7/1043 |
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| Summary: | The disposal and recycling of industrial by-products such as marble and limestone powders pose pressing environmental challenges due to the substantial amounts of waste generated annually by marble processing plants and limestone quarries. The integration of these by-products into concrete production is justified by their widespread availability and the potential to alleviate the environmental burden. This study used a statistical mixture design approach to systematically assess the effects of limestone and marble powders, with varying fineness levels, as partial cement replacements (up to 17%) on the rheological and mechanical properties of self-compacting concrete (SCC). The experimental findings revealed that the density of the SCC mixtures ranged from 2475 to 2487 kg/m<sup>3</sup>. Mixtures incorporating limestone powder exhibited superior flowability, achieving a slump flow of up to 69 cm, an 8% improvement compared to those containing marble powder. However, marble powder with a specific surface area of 330 m<sup>2</sup>/kg demonstrated significant improvements in compressive and tensile strengths, with increases of 18%. Statistical analysis using analysis of variance (ANOVA) validated the reliability of the predictive models developed, which demonstrated coefficients of determination (R<sup>2</sup>) that exceeded 0.94 and <i>p</i>-values below 0.05. These models enable precise predictions of critical performance metrics, including density, slump flow, box flow, compressive strength, and tensile strength, thus reducing the need for extensive experimental procedures. |
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| ISSN: | 2075-5309 |