We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Influence of Fine Recycled Concrete Powder on the Compressive Strength of Self-Compacting Concrete (SCC) Using Artificial Neural Network.
- Authors
Boudali, Sara; Abdulsalam, Bahira; Rafiean, Amir Hossein; Poncet, Sébastien; Soliman, Ahmed; ElSafty, Adel; Nocera, Francesco; Caponetto, Rosa Giuseppina; Giuffrida, Giada; Detommaso, Maurizio
- 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.
- Subjects
ARTIFICIAL neural networks; COMPRESSIVE strength; SELF-consolidating concrete; CONCRETE; FLY ash
- Publication
Sustainability (2071-1050), 2021, Vol 13, Issue 6, p3111
- ISSN
2071-1050
- Publication type
Article
- DOI
10.3390/su13063111