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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/4229
Title: Artificial neural network for predicting creep and shrinkage of high performance concrete
Authors: Karthikeyan J.
Upadhyay A.
Bhandari N.M.
Published in: Journal of Advanced Concrete Technology
Abstract: Concrete undergoes time-dependent deformations that must be considered in the design of reinforced/prestressed highperformance 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. Copyright ¬© 2008 Japan Concrete Institute.
Citation: Journal of Advanced Concrete Technology(2008), 6(1): 135-142
URI: https://doi.org/10.3151/jact.6.135
http://repository.iitr.ac.in/handle/123456789/4229
Issue Date: 2008
ISSN: 13468014
Author Scopus IDs: 57200577120
13608643500
7103395363
Author Affiliations: Karthikeyan, J., Department of Civil Engineering, Indian Institute of Technology, Roorkee, India
Upadhyay, A., Department of Civil Engineering, Indian Institute of Technology, Roorkee, India
Bhandari, N.M., Department of Civil Engineering, Indian Institute of Technology, Roorkee, India
Corresponding Author: Karthikeyan, J.; Department of Civil Engineering, Indian Institute of Technology, Roorkee, India; email: karthi1212@gmail.com
Appears in Collections:Journal Publications [CE]

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