Author(s): Amina Al-Farsi and Hassan Al-Mutairi

Abstract: Concrete is the most widely used construction material, but its heavy reliance on natural aggregates poses significant environmental challenges, including resource depletion and waste generation. This study explores the development of a multiscale modeling framework for solid waste aggregate (SWA) concrete, aimed at addressing performance limitations while promoting sustainability. The primary objective was to examine the mechanical and durability properties of concrete with varying SWA replacement ratios (0%, 25%, 50%, and 75%) and validate these findings using computational and experimental approaches. Materials included construction and demolition waste-derived SWAs, ordinary Portland cement, and supplementary cementitious materials (SCMs) like fly ash and silica fume. Concrete specimens were tested for compressive strength, water absorption, and durability under standardized laboratory conditions. A multiscale modeling framework was developed to simulate interactions at the microstructural level using finite element analysis (FEA), linking these properties to macro-level performance metrics. The results revealed that increasing SWA replacement ratios reduced compressive strength and durability due to higher porosity and weaker interfacial transition zones (ITZ). The 75% replacement ratio showed a 28.6% reduction in compressive strength at 28 days compared to the control, with a strong negative correlation (r = -0.85). Water absorption and chloride ion permeability increased significantly at higher SWA content, reflecting durability challenges. However, the use of SCMs mitigated some adverse effects, and microstructural analysis validated the model predictions. This study concludes that SWA concrete is a sustainable alternative, but performance optimization through pre-treatment methods, SCM integration, and tailored mix designs is essential. Future research should address durability under real-world conditions and leverage advanced modeling techniques to improve predictive accuracy. These findings contribute to advancing sustainable construction practices through innovative material design and modeling.

DOI: 10.22271/27078361.2024.v5.i2a.55

Pages: 31-36 | Views: 61 | Downloads: 16

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