Optimization of fresh and mechanical properties of sustainable concrete composite containing ARGF and fly ash: An application of response surface methodology

• Published in: Construction & building materials 2023-01, Vol.362, p.129722, Article 129722
• Main Authors: Raheel Shah, Syyed Adnan, Ben Kahla, Nabil, Atig, Miniar, Kashif Anwar, Muhammad, Azab, Marc, Mahmood, Atif
• Format: Article
• Language: English
• Subjects: ARGF; Eco-Friendly wastes; Fly Ash; Mechanical properties; Sustainability; Sustainable concrete
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2022.129722

•Carbon neutral concrete production is future of construction industry.•RSM is a well-known mathematical or statistical predictive tool.•Optimum use of waste materials in combination with fibers can help in developing sustainable concrete.•RSM has been used to forecast the desired qualities of concrete by obtaining an optimal mixed proportion. Globally, significant attempts have been made to properly utilize waste materials and various by-products as supplementary cementitious materials (SCMs) in the production of sustainable concrete. Fly ash and alkali resistance Glass Fiber (ARGF) are two ingredients that can help to improve the overall performance of concrete and mortars. The main objective of this study is to use Response Surface Methodology (RSM) to predict and optimize the fresh and mechanical properties of modified concrete made in combination with Fly Ash as a cement alternative and ARGF as extra reinforcing material. The different percentages of both materials Fly ash and ARGF were chosen as design factors to develop statistical models for the responses of fresh and mechanical parameters at 7 and 28 days. The experimental runs were carried out utilizing Central Composite Design (CCD), and there was a strong connection between the actual and estimated values. At a 95 % level of confidence, the effect of multiple factors on the target response was studied using statistical analysis of ANOVA results. The coefficient of determination (R2) values ranging from 0.82 to 0.99 show the reliability and performance of the proposed model. The optimal nine responses values for the slump, 7D-CS, 28D-CS, 7D-STS, 28D-STS, 7D-FS, 28D-FS, 7D-UPV and 28D-UPV were 41.70, 1820.51, 2177.23,291.86, 378.85, 589.656, 756.58, 3.82, and 4.34 with desirability value of 0.918. The linearity of the data points surrounding the line of fit for each mix blends indicating the model’s efficiency in estimating the nine responses. This study will help in optimization of concrete mix design using waste materials and fibers.