Prediction of properties of self-compacting concrete containing fly ash using artificial neural network

This paper investigates the feasibility of using artificial neural networks (ANNs) modeling to predict the properties of self-compacting concrete (SCC) containing fly ash as cement replacement. For the purpose of constructing this model, a database of experimental data was gathered from the literatu...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications 2017-12, Vol.28 (Suppl 1), p.707-718
Main Authors: Belalia Douma, Omar, Boukhatem, Bakhta, Ghrici, Mohamed, Tagnit-Hamou, Arezki
Format: Article
Language:English
Subjects:
Aggregates
Artificial Intelligence
Artificial neural networks
Compressive strength
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Feasibility studies
Fly ash
Image Processing and Computer Vision
Mathematical models
Neural networks
Original Article
Prediction models
Probability and Statistics in Computer Science
Properties (attributes)
Self-compacting concrete
Sensitivity analysis
Studies
Time compression
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper investigates the feasibility of using artificial neural networks (ANNs) modeling to predict the properties of self-compacting concrete (SCC) containing fly ash as cement replacement. For the purpose of constructing this model, a database of experimental data was gathered from the literature and used for training and testing the model. The data used in the artificial neural network model are arranged in a format of six input parameters that cover the total binder content, fly ash replacement percentage, water–binder ratio, fine aggregates, coarse aggregates and superplasticizer. Four outputs parameters are predicted based on the ANN technique as the slump flow, the L-box ratio, the V-funnel time and the compressive strength at 28 days of SCC. To demonstrate the utility of the proposed model and improve its performance, a comparison of the ANN-based prediction model with other researcher’s experimental results was carried out, and a good agreement was found. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on SCC properties. This study shows that artificial neural network has strong potential as a feasible tool for predicting accurately the properties of SCC containing fly ash.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2368-7