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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 10/2022

06-04-2022 | Research Article-Civil Engineering

Evolutionary Artificial Intelligence Model to Formulate Compressive Strength of Eco-friendly Concrete Containing Recycled Polyethylene Terephthalate

Authors: Mahdi MirzagoltabarRoshan, Mohammadhadi AlizadeElizei, Reza Esmaeilabadi

Published in: Arabian Journal for Science and Engineering | Issue 10/2022

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Abstract

In this research, evolutionary formula-based models were developed for designing of eco-friendly concrete containing recycled polyethylene terephthalate (PET). To do so, evolutionary artificial intelligence (AI) approach was implemented based on the integration of the multivariate adaptive regression splines (MARS) and particle swarm optimization algorithm for estimation of compressive strength (CS) of eco-friendly concrete containing recycled PET. The experimental database consisting 320 records comprising mixture components at different ages was collected from published documents. The capability and efficiency of proposed model were validated through different AI methods including linear regression, extreme learning machine, random forest, M5p model tree and standalone MARS. Performance metrics indicated that proposed evolutionary model with the optimized parameters outperformed other benchmark models in term of accuracy. Uncertainty analysis of the standalone and hybridized models were also applied using Monte-Carlo simulation to prove that the model has less uncertainty in the prediction of the CS compared to those benchmark models. The findings of the paper presented the superiority of the model’s development in constructing reasonable and robustness evolutionary model for formulation of CS of eco-friendly concrete.
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Appendix
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Metadata
Title
Evolutionary Artificial Intelligence Model to Formulate Compressive Strength of Eco-friendly Concrete Containing Recycled Polyethylene Terephthalate
Authors
Mahdi MirzagoltabarRoshan
Mohammadhadi AlizadeElizei
Reza Esmaeilabadi
Publication date
06-04-2022
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 10/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06432-7

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