Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

Itzel Nunez; Afshin Marani; Moncef L. Nehdi

doi:10.3390/ma13194331

journal article Open Access Sep 29, 2020

Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

Itzel Nunez

Afshin Marani

Moncef L. Nehdi

Materials Vol. 13 No. 19 pp. 4331 · MDPI AG

View at Publisher Save 10.3390/ma13194331

Abstract

Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However, complexities in the mixture optimization of RAC due to the variability of recycled aggregates and lack of accuracy in estimating its compressive strength require novel and sophisticated techniques. This paper aims at developing state-of-the-art machine learning models to predict the RAC compressive strength and optimize its mixture design. Results show that the developed models including Gaussian processes, deep learning, and gradient boosting regression achieved robust predictive performance, with the gradient boosting regression trees yielding highest prediction accuracy. Furthermore, a particle swarm optimization coupled with gradient boosting regression trees model was developed to optimize the mixture design of RAC for various compressive strength classes. The hybrid model achieved cost-saving RAC mixture designs with lower environmental footprint for different target compressive strength classes. The model could be further harvested to achieve sustainable concrete with optimal recycled aggregate content, least cost, and least environmental footprint.

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References

Details

Published: Sep 29, 2020
Vol/Issue: 13(19)
Pages: 4331
License: View

Authors

I

Itzel Nunez

Department of Civil and Environmental Engineering, Western University, London, ON N6G 1G8, Canada

A

Afshin Marani

Department of Civil and Environmental Engineering, Western University, London, ON N6G 1G8, Canada

M

Moncef L. Nehdi

Department of Civil and Environmental Engineering, Western University, London, ON N6G 1G8, Canada

Cite This Article

Itzel Nunez, Afshin Marani, Moncef L. Nehdi (2020). Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model. Materials, 13(19), 4331. https://doi.org/10.3390/ma13194331

Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

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