%O cited By 5
%J Structures
%L scholars16121
%D 2022
%R 10.1016/j.istruc.2022.11.013
%X The abundance of waste foundry sand (WFS) produced by the foundry industry has become a global issue. As a result, foundry waste management and disposal are getting more complex, necessitating more extensive and inventive efforts. The purpose of this study was to use WFS as a partial replacement to reduce the use of fine aggregate in various concrete mixtures and to evaluate fresh concrete performance such as slump and mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS). WFS was adjusted using the Design-Expert software's Central Composite Design (CCD) tool in Response Surface Methodology (RSM). The optimization process investigated the interaction between WFS ratio and curing days on the mechanical properties of concrete. The responses of the optimization process were the CS, STS, and FS, which were generated by the quadratic regression model created by ANOVA. The WFS was replaced in 10 increments from 0 to 40. The highest mechanical properties were achieved at 20 replacement and 56 days of curing with a CS of 29.37 MPa, STS of 3.828 MPa, and FS of 8.0 MPa. The quadratic model was suggested for the three responses by RSM, in which the coefficient of determination (R2) ranges from 0.987 to 0.995, showing the model's high significance. Up to a 30 replacement level, the fresh qualities of all substitutes were nearly identical to the control mix. So, 20 replacement is the optimum replacement level, and 30 is the general replacement level. As a result, it can be inferred that WFS can replace 20 of natural fine aggregate in order to obtain normal concrete strength. In contrast, for non-structural concrete, WFS can replace 30 of natural sand, which improves environmental sustainability. © 2022 Institution of Structural Engineers
%P 1581-1594
%T Central composite design application in the optimization of the effect of waste foundry sand on concrete properties using RSM
%A M. Ali
%A M.I. Khan
%A F. Masood
%A B.T. Alsulami
%A B. Bouallegue
%A R. Nawaz
%A R. Fediuk
%I Elsevier Ltd
%V 46