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

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概要

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• 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.

• 公益社団法人 日本コンクリート工学会の論文

著者

• Upadhyay Akhil

  Department of Civil Engineering, I.I.T. Roorkee, India.

• Karthikeyan Jayakumar

  Department of Civil Engineering, Indian Institute of Technology, Roorkee, India.

• Upadhyay Akhil

  Department of Civil Engineering, Indian Institute of Technology, Roorkee, India.

• Bhandari Navaratan

  Department of Civil Engineering, Indian Institute of Technology, Roorkee, India.

関連論文

• Artificial Neural Network for Predicting Creep and Shrinkage of High Performance Concrete
• Simplified Design of Composite Slabs Using Slip Block Test

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