Characterization of sustainable concrete made from wastewater bottle caps using a machine learning and RSM-CCD: towards performance and optimization

Characterization of sustainable concrete made from wastewater bottle caps using a machine learning and RSM-CCD: towards performance and optimization

Nayeemuddin Mohammed, Andi Asiz, Hiren Mewada, Zahara Begum, Salma Begum, Shahana Khatun, Tasneem Sultana

Abstract. The properties of concrete, a widely used building material across the globe, have changed due to technological breakthroughs. Cement, sand, coarse aggregate, and water are the four components used to build concrete. Technological improvements increase human comfort, yet the environment is also harmed. Therefore, recycling and reuse are vital to environmental engineers because they help reduce the amount of plastic bottle garbage disposed of as solid waste. In this study, water-cement ratios of 0.5, 0.55, and 0.6 are used in lieu of concrete in various percentages, including 0, 6, and 12% of coarse aggregate replaced by water bottle caps, to analyze the behavior of concrete’s compressive strength experimentally. Based on experimental results, models based on artificial neural networks—Levenberg Marquardt and Response Surface Methodology—Central Composite Design models were developed to forecast the final compressive strength of concrete made in part from plastic water bottles. The results demonstrate that for accurately predicting the properties of concrete, the ANN-LM model yields the best result, R2=0.98, which is close to 1 and R2 = 0.85 for RSM-CCD, respectively.

Keywords
Plastic Bottle Caps, Compressive Strength, Artificial Neural Network, Central Composite Design

Published online 9/25/2023, 9 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Nayeemuddin Mohammed, Andi Asiz, Hiren Mewada, Zahara Begum, Salma Begum, Shahana Khatun, Tasneem Sultana, Characterization of sustainable concrete made from wastewater bottle caps using a machine learning and RSM-CCD: towards performance and optimization, Materials Research Proceedings, Vol. 36, pp 38-46, 2023

DOI: https://doi.org/10.21741/9781644902790-4

The article was published as article 4 of the book AToMech1-2023 Supplement

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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