Open Access

Enhancing Concrete Sustainability and Strength: Utilization of Industrial By-products in Tetranary Blended Nano Concrete

Myakala Vamshikrishna, vamshikrishna.myakala1008@gmail.com
Department of Structural Engineering, Annamalai University, Chidambaram, TN, India Ilango Sivakumar Assistant Professor, Department of Structural Engineering, Annamalai university, Chidambaram, Tamil Nadu, India

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Abstract

The manufacturing process of ordinary portland cement depletes natural resources like limestone quickly and emits significant amounts of CO2 into the environment due to the high energy consumption during calcination. Current research aims to identify alternative cementitious materials to replace Ordinary Portland Cement (OPC). Building on earlier investigations, this study emphasizes the use of industrial by-products rich in alumina and silica as partial substitutes for OPC. Specifically, it examines the effects of integrating materials such as fly ash and colloidal nano silica (CNS) into concrete as partial cement replacements and utilizing manufactured sand (M-sand) as a full substitute for fine aggregate. The research introduces a Tetranary Blended Nano Concrete mix that combines 30% fly ash and varying nano silica levels (1%, 2%, and 3%) with 100% M-sand. The compressive strength of an M30-grade concrete mix was tested over a curing period of up to 90 days. Findings indicate that incorporating nano silica significantly improves the compressive strength of Tetranary Blended Nano Concrete. Furthermore, the complete substitution of fine aggregate with M-sand not only enhanced the concrete's mechanical properties but also contributed to its sustainability. The addition of nano silica to fly ash significantly enhances early strength and provides moderate improvement in overall compressive strength. To validate the compressive strength values obtained, the Response Surface Method was employed for mathematical modelling and statistical analysis, allowing for the prediction of compressive strength values from the regression equation and comparison with experimental data.

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