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Influence of Fine Recycled Concrete Powder on the Compressive Strength of Self-Compacting Concrete (SCC) Using Artificial Neural Network

Author

Listed:
  • Sara Boudali

    (Mechanical Engineering Department, Faculty of Engineering, Université de Sherbrooke and Groupe ABS, Sherbrooke, QC J1L 2G7, Canada)

  • Bahira Abdulsalam

    (CIISolutions Composites Infrastructure Innovation Solutions Corp., Toronto, ON M4H1L6, Canada)

  • Amir Hossein Rafiean

    (Department of Soil and Foundation Engineering, Civil Engineering Faculty, Semnan University, Semnan 35196, Iran)

  • Sébastien Poncet

    (Mechanical Engineering Department, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • Ahmed Soliman

    (Building, Civil, and Environmental Engineering, Concordia University, Montréal, QC H3G 1M8, Canada)

  • Adel ElSafty

    (School of Engineering, Civil Engineering, University of North Florida, Jacksonville, FL 32224, USA)

Abstract

This paper aims to investigate the effect of fine recycled concrete powder (FRCP) on the strength of self-compacting concrete (SCC). For this purpose, a numerical artificial neural network (ANN) model was developed for strength prediction of SCC incorporating FRCP. At first, 240 experimental data sets were selected from the literature to develop the model. Approximately 60% of the database was used for training, 20% for testing, and the remaining 20% for the validation step. Model inputs included binder content, water/binder ratio, recycled concrete aggregates’ (RCA) content, percentage of supplementary cementitious materials (fly ash), amount of FRCP, and curing time. The model provided reliable results with mean square error (MSE) and regression values of 0.01 and 0.97, respectively. Additionally, to further validate the model, four experimental recycled self-compacting concrete (RSCC) samples were tested experimentally, and their properties were used as unseen data to the model. The results showed that the developed model can predict the compressive strength of RSCC with high accuracy.

Suggested Citation

  • Sara Boudali & Bahira Abdulsalam & Amir Hossein Rafiean & Sébastien Poncet & Ahmed Soliman & Adel ElSafty, 2021. "Influence of Fine Recycled Concrete Powder on the Compressive Strength of Self-Compacting Concrete (SCC) Using Artificial Neural Network," Sustainability, MDPI, vol. 13(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3111-:d:515507
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    Citations

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    Cited by:

    1. Emerson Felipe Felix & Edna Possan & Rogério Carrazedo, 2021. "A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    2. Zhenwen Hu & Zhe Kong & Guisheng Cai & Qiuyi Li & Yuanxin Guo & Dunlei Su & Junzhe Liu & Shidong Zheng, 2021. "Study of the Properties of Full Component Recycled Dry-Mixed Masonry Mortar and Concrete Prepared from Construction Solid Waste," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    3. Kaiyue Zhao & Peng Zhang & Bing Wang & Yupeng Tian & Shanbin Xue & Yuan Cong, 2021. "Preparation of Electric- and Magnetic-Activated Water and Its Influence on the Workability and Mechanical Properties of Cement Mortar," Sustainability, MDPI, vol. 13(8), pages 1-17, April.

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