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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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. [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.
AbstractList	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. 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.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. 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.
ArticleNumber	146524
Author	Iqbal, Muhammad Farjad Javed, Muhammad Faisal Yang, Jian Rauf, Momina Ashraf, Muhammad Liu, Qing-feng Azim, Iftikhar
Author_xml	– sequence: 1 givenname: Muhammad Farjad surname: Iqbal fullname: Iqbal, Muhammad Farjad organization: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 2 givenname: Muhammad Faisal surname: Javed fullname: Javed, Muhammad Faisal organization: Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan – sequence: 3 givenname: Momina surname: Rauf fullname: Rauf, Momina organization: Military College of Engineering, NUST, Risalpur 24080, Pakistan – sequence: 4 givenname: Iftikhar surname: Azim fullname: Azim, Iftikhar organization: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 5 givenname: Muhammad surname: Ashraf fullname: Ashraf, Muhammad organization: Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23460, Swabi, Pakistan – sequence: 6 givenname: Jian surname: Yang fullname: Yang, Jian organization: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 7 givenname: Qing-feng surname: Liu fullname: Liu, Qing-feng email: liuqf@sjtu.edu.cn organization: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Keywords	Landfill disposal Solid waste Multi-expression programming Parametric analysis Sustainable construction Foundry sand
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Snippet	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...
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SubjectTerms	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
Title	Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming
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