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

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...

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Veröffentlicht in:The Science of the total environment Jg. 780; S. 146524
Hauptverfasser: Iqbal, Muhammad Farjad, Javed, Muhammad Faisal, Rauf, Momina, Azim, Iftikhar, Ashraf, Muhammad, Yang, Jian, Liu, Qing-feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2021
Schlagworte:
artificial intelligence
compression strength
concrete
environment
Foundry sand
industry
Landfill disposal
model validation
modulus of elasticity
Multi-expression programming
Parametric analysis
prediction
sand
sand fraction
Solid waste
solid wastes
statistical analysis
Sustainable construction
tensile strength
waste disposal
Landfill disposal
Solid waste
Multi-expression programming
Parametric analysis
Sustainable construction
Foundry sand
ISSN:0048-9697, 1879-1026, 1879-1026
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Zusammenfassung: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. [Display omitted] •WFS, a hazardous solid waste, causes adverse environmental impact.•Artificial intelligence is used to model mechanical properties of green concrete.•Validation and parametric study is performed to ensure the accuracy of models.•Application of proposed models can contribute towards sustainable construction.
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ISSN:0048-9697
1879-1026
1879-1026
DOI:10.1016/j.scitotenv.2021.146524