Statistical Modeling and Multi-Response Optimization of Sustainable Recycled Aggregate Concrete Using Response Surface Methodology

A. I. Eldahshor; Nasser Alanazib; Yazid Chetbanic; Ali AlAteahd; Hisham Fawzy

doi:10.66279/xw89r318

Authors

A.I. Eldahshor Beni-Suef University Author
Nasser Alanazib University of Ha'il Author
Yazid Chetbanic Scientific and Technical Research Centre in Physico-Chemical Analyses Author
Ali H. AlAteahd University of Hafr Al-Batin Author
Hisham Fawzy Beni-Suef University Author

DOI:

https://doi.org/10.66279/xw89r318

Keywords:

Recycled aggregate concrete, Response surface methodology (RSM);, Multi-response optimization, Compressive strength, Desirability function

Abstract

This study investigates the influence of natural aggregate (A), recycled aggregate (B), and treatment type (C) on slump and compressive strength at 7, 28, and 90 days. A quadratic response surface methodology (RSM) was applied to model linear, interaction, and quadratic effects. ANOVA confirmed model significance (p < 0.0001), with high F-values. Natural aggregate (A) was the dominant factor (F = 547.60 for slump, 425.74 for CS7, and 485.30 for CS90), while recycled aggregate (B) showed negligible influence within the studied range. Treatment type (C) exhibited significant nonlinear effects through its quadratic term (C²). The models demonstrated excellent predictive capability (R² = 0.9965 for slump, 0.9941 for CS7, and 0.9943 for CS90), with low standard deviation (≤ 0.7591) and high adequate precision (up to 60.336). Residual diagnostics confirmed normality, independence, and absence of bias. Response surface analysis indicated that increasing natural aggregate enhances both workability and strength. Optimization identified optimal conditions at A = [A*], B = [B*], and C = [C*], achieving a desirability of D = [D*]. These findings provide a robust statistical basis for optimizing sustainable concrete mixtures incorporating recycled aggregates.

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Author Biographies

A.I. Eldahshor, Beni-Suef University

Department of mechanical Engineering, Faculty of Engineering, Beni-Suef University, Beni-Suef 62511, Egypt
Nasser Alanazib, University of Ha'il

Civil Engineering Department, College of Engineering, University of Ha’il, Ha’il 55474, Saudi Arabia
Yazid Chetbanic, Scientific and Technical Research Centre in Physico-Chemical Analyses

Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques CRAPC, BP 384, Bou-Ismail, RP 42004, Tipaza, Algeria,
Ali H. AlAteahd, University of Hafr Al-Batin

Department of Civil Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
Hisham Fawzy, Beni-Suef University

Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt.

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