
Increased industrialization has resulted in a shortage of natural building materials, thus increasing awareness of sustainable approaches by construction companies. This research explains how waste materials—Ceramic Waste Powder, Waste Glass Powder, Waste Granite Dust, Waste Marble Powder, and Waste Brick Powder—can be employed as environmentally friendly cement alternatives in concrete mixtures. The objective is to study the mechanical characteristics of these supplementary cementitious materials with continuous industrial waste recycling for environmentally sustainable development. In addition to experimental findings, a neural network model was developed to predict the compressive strength of concrete containing these materials, trained on data collected from the literature. The model successfully demonstrated its ability to replicate trends in compressive strength results across varying replacement levels, validating the findings and enhancing the study’s reliability. Tests were carried out for replacement levels of cement by the materials in concrete, from 5 % to 50 %, on compressive and tensile strengths at various curing periods. The test results show that a 10–15 % replacement level is within the optimum range for most of the waste materials. It is also observed that compressive and tensile strength improvement tends to be maximum around 28 days of curing. Increases in dosage lead to a loss in mechanical properties, indicating limited viability for higher replacement percentages. The present review, supported by machine learning predictions, highlights the potential of these materials to improve sustainable practices in the building industries, toward manufacturing Supplementary Cementitious Materials (SCMs) with low environmental impact coupled with resource efficiency.
Standardization. Simplification. Waste, HD62, Neural Networks, Supplementary Cementitious Materials (SCMs), Waste Materials, Concrete durability, Mechanical Properties, Cement Replacement, Environmental technology. Sanitary engineering, TD1-1066
Standardization. Simplification. Waste, HD62, Neural Networks, Supplementary Cementitious Materials (SCMs), Waste Materials, Concrete durability, Mechanical Properties, Cement Replacement, Environmental technology. Sanitary engineering, TD1-1066
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