Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming

Muhammad Farjad Iqbal; Muhammad Faisal Javed; Momina Rauf; Iftikhar Azim; Muhammad Ashraf; Jian Yang; Qing-Feng Liu

doi:10.1016/j.scitotenv.2021.146524

Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming

Sci Total Environ. 2021 Aug 1:780:146524. doi: 10.1016/j.scitotenv.2021.146524. Epub 2021 Mar 18.

Authors

Muhammad Farjad Iqbal¹, Muhammad Faisal Javed², Momina Rauf³, Iftikhar Azim⁴, Muhammad Ashraf⁵, Jian Yang⁴, Qing-Feng Liu⁶

Affiliations

¹ State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23460, Swabi, Pakistan.
² Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan.
³ Military College of Engineering, NUST, Risalpur 24080, Pakistan.
⁴ State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
⁵ Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23460, Swabi, Pakistan.
⁶ State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: liuqf@sjtu.edu.cn.

PMID: 34030334
DOI: 10.1016/j.scitotenv.2021.146524

Abstract

Waste Foundry sand (WFS), a major solid waste from metal casting industry, is posing a significant environmental threat owing to its disposal to landfills. In this research, an innovative artificial intelligence technique i.e. Multi-Expression Programming (MEP) is applied to model the split tensile strength (ST) and modulus of elasticity (E) of concrete containing waste foundry sand (CWFS). The presented formulations correlate mechanical properties with four input variables i.e. w/c, foundry sand content, superplasticizer content and compressive strength. The results of statistical analysis validate the model accuracy as evident by the low values of objective function (0.033 for E and 0.052 for ST). Moreover, the average error in the predicted values is significantly low i.e. 0.287 MPa and 1.75 GPa for ST and E model, respectively. Parametric study depicts that the models are well trained to accurately predict the trends of mechanical properties with variation in mix parameters. The prediction models can promote the usage of WFS in green concrete thereby preventing waste disposal and contributing towards and sustainable construction.

Keywords: Foundry sand; Landfill disposal; Multi-expression programming; Parametric analysis; Solid waste; Sustainable construction.