Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming

Abstract

Waste foundry sand (WFS) is a major pollutant generated from metal casting foundries and is classified as a hazardous material due to the presence of organic and inorganic pollutants which can cause adverse environmental impact. In order to promote the re-utilization of WFS, gene expression programming (GEP) has been employed in this study to develop empirical models for prediction of mechanical properties of concrete made with WFS (CMWFS). An extensive and reliable database of mechanical properties of CMWFS is established through a comprehensive literature review. The database comprises of 234 compressive strength, 163 split tensile strength and 85 elastic modulus results. The four most influential parameters i.e. water-to-cement ratio, WFS percentage, WFS-to-cement content ratio and fineness modulus of WFS are considered as the input parameters for modelling. The mechanical properties can be estimated by the application of proposed simplified mathematical expressions. The performance of the models is assessed by conducting parametric analysis, applying statistical checks and comparing with regression models. The results reflected that the proposed models are accurate and possess a high generalization and prediction capability. The findings of this study can enhance the re-usage of WFS for development of green concrete leading to environmental protection and monetary benefits.

Keywords: Compressive strength; Gene expression programming; Green concrete; Split tensile strength; Waste foundry sand.