Artificial Neural Network for Predicting Creep and Shrinkage of High Performance Concrete
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete Properties and Behavior · Infrastructure Maintenance and Monitoring · Concrete and Cement Materials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Indian Institute of Technology, Roorkee, India.
References (15)
Show source-provided references
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