Experimental Investigation and Predictive Modelling of Sustainable Concrete Incorporating Industrial By-Products
DOI:
https://doi.org/10.22399/ijcesen.4824Keywords:
GGBS, Fly ash, Marble dust, Laterite, Machine learning, Mechanical propertiesAbstract
The increasing demand for concrete structures has led to the overexploitation of natural resources such as river sand and stone aggregates. This study explores sustainable alternatives by incorporating industrial by-products like fly ash and marble dust, along with laterite and demolition debris, as partial replacements in M30-grade concrete. Ground Granulated Blast Furnace Slag (GGBS) was used as an ad IJ Iditive in all mix proportions. Concrete mixes were prepared and tested for compressive, flexural, split tensile strengths water absorption, sorptivity and RCPT. The 10% fly ash mix achieved a compressive strength of 45.09 MPa after 28 days, closely matching the control mix (46.74 MPa). However, 20% and 30% marble dust replacements resulted in reduced strengths of 28.65 MPa and 29.84 MPa, respectively. The 10% laterite mix exhibited the highest compressive strength (48.46 MPa), surpassing conventional concrete. Flexural and split tensile strength results followed similar trends, with fly ash and laterite improving mechanical properties, while excessive marble dust reduced tensile performance. To enhance predictive accuracy and optimize mix design, machine learning models were employed. The Random Forest Regression model demonstrated the highest prediction accuracy (R²= 0.92), outperforming traditional regression models. The integration of machine learning enabled efficient analysis, identifying key parameters affecting concrete performance. This study highlights the potential of sustainable materials in concrete production and underscores the role of data-driven approaches in optimizing mechanical properties for eco-friendly construction.
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