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

Sara Boudali, Bahira Abdulsalam, Amir Hossein Rafiean, Sébastien Poncet, Ahmed Soliman and Adel ElSafty
Additional contact information
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

Sustainability, 2021, vol. 13, issue 6, 1-28

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.

Keywords: artificial neural network; self-compacting concrete; compressive strength; fine recycled concrete powder; fly ash (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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