Comprehensive experimental and numerical modeling of strength parameters of eco-friendly steel fiber reinforced SCC containing coarse copper slag aggregates

[Display omitted] •Producibility of green, SFRSCC containing CS has been investigated.•CS can improve the fresh concrete characteristics and strengthen its microstructure.•Increasing the SF volume affects the concrete performance negatively and improves the strength parameters.•Sensitivity analyses...

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Veröffentlicht in:Construction & building materials 2023-02, Vol.367, p.130304, Article 130304
Hauptverfasser: Afshoon, Iman, Miri, Mahmoud, Mousavi, Seyed Roohollah
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Sprache:eng
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Zusammenfassung:[Display omitted] •Producibility of green, SFRSCC containing CS has been investigated.•CS can improve the fresh concrete characteristics and strengthen its microstructure.•Increasing the SF volume affects the concrete performance negatively and improves the strength parameters.•Sensitivity analyses have shown steel fibers are more effective on strength parameters.•ANN models perform better than MRA models in predicting the strength parameters. The copper slag waste (CSW) is an industrial by-product, the annual production of which is huge and its disposal and management are a big challenge for environmentalists. This research has comprehensively studied, experimentally and numerically, the possibility of substituting 20–60% copper slag (CS) for natural coarse aggregates in self-compacting concretes (SCCs) reinforced with 0.1, 0.3 and 0.5% hooked-end steel fibers. According to the results, increasing the CS volume improves the characteristics of the fresh concrete, but there is an obvious drop in its flowability, fillability and passability at the highest steel fiber (SF) volume. Although increasing CS reduces the compressive strength of concretes containing low SF volumes, positive CS effects on the concrete strength are evident at the highest SF volume. Based on the scanning electron microscope (SEM) images, in concretes containing 0.1% SF, an increase in CS increases the tensile strength, and increasing this volume up to 30% makes the concrete microstructure homogeneous and integrated. To check the statistical importance of the results of the tensile and compressive strengths, use was made of the analysis of variance (ANOVA) method. To reduce costs (and human errors) and avoid time wastage, artificial neural network (ANN) prediction models and robust multiple regression analyses (MRA) methods were used to predict the compressive and tensile strengths. Results of this research confirm that using CS in producing steel fiber reinforced self-compacting concrete (SFRSCC) has technical, environmental and economic advantages.
ISSN:0950-0618
1879-0526
DOI:10.1016/j.conbuildmat.2023.130304