Kurdish Studies

ISSN: 2051-4883 | e-ISSN: 2051-4891
Email: editor@kurdishstudies.net

Optimization and Stochastic Modelling of Process Parameters to Predict Eco-Friendly and Sustainable Concrete Using Recycled Aggregate

J. Rajprasad
Department of civil engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur -603203, Tamil Nadu, India.
K. S. Anandh
Department of civil engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur -603203, Tamil Nadu, India.
Musa Adamu
Engineering Management Department, College of Engineering, Prince Sultan University, 11586 Riyadh, Saudi Arabia.
Omar Shabbir Ahmed
Keywords: Construction/Demolition waste; Sustainable building; Recycled aggregates; Taguchi method; Grey Relational Grade..

Abstract

Environmental sustainability is mandating technologically supported eco-friendly alternatives in an attempt to uncover a plethora of possibilities. A critical stage towards attaining an environmentally sustainable product involves formulating and assessing the product by adopting appropriate technology or formula. In the current research, an eco-friendly blend containing recycled aggregates was analyzed and monitored using the Grey Relational Grade (GRG) analysis in the Taguchi optimization technique. Three influencing parameters, involves water/cement ratio, volume ratio of recycled coarse aggregate, and incubation days (curing days), were selected to investigate the grey relational grade analysis based on two responses (compressive strength and split tensile strength). Data were collected on 7, 14, and 28 days of curing (incubation) to determine the ideal combination of recycled aggregate concrete. SEM examination after compression tests revealed the presence of microcracks and tiny pores, demonstrating that recycled aggregate has the same direct impact as recycled aggregate concrete. The GRG method predicted an optimal combination (Run 07) of A1B1C3 (25% recycled aggregate, 0.2 W/C ratio, and 28-day incubation time). To validate the model's adequacy, a validation experimental run based on run 07's conditions (A1B1C3) was performed. ANOVA analysis indicated that incubation days were found to be an influential factor in GRG.

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