Artificial Neural Network for Predicting Creep and Shrinkage of High Performance Concrete

Concrete undergoes time-dependent deformations that must be considered in the design of reinforced/prestressed high-performance concrete (HPC) bridge girders. In this research, experiments on the creep and shrinkage properties of a HPC mix were conducted for 500 days. The test results obtained from...

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Bibliographic Details
Published in:Journal of Advanced Concrete Technology 2008, Vol.6(1), pp.135-142
Main Authors: Karthikeyan, Jayakumar, Upadhyay, Akhil, Bhandari, Navaratan M.
Format: Article
Language:English
Subjects:
Creep (materials)
Learning theory
Mathematical models
Neural networks
Reinforcing steels
Shrinkage
Strain
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Summary:Concrete undergoes time-dependent deformations that must be considered in the design of reinforced/prestressed high-performance concrete (HPC) bridge girders. In this research, experiments on the creep and shrinkage properties of a HPC mix were conducted for 500 days. The test results obtained from this research were compared to different models to determine which model was the better one. The CEB-90 model was found better in predicting time-dependent strains and deformations for the above HPC mix. However, in a far zone, some deviation was observed, and to get a better model, the experimental data base was used along with the CEB-90 model database to train the neural network. The developed Artificial Neural Network (ANN) model will serve as a more rational as well as computationally efficient model in predicting creep coefficient and shrinkage strain.
ISSN:1346-8014
1347-3913
1347-3913
DOI:10.3151/jact.6.135