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http://dx.doi.org/10.12989/sem.2014.52.1.097

An apt material model for drying shrinkage and specific creep of HPC using artificial neural network  

Gedam, Banti A. (Civil Engineering Department, Indian Institute of Technology)
Bhandari, N.M. (Civil Engineering Department, Indian Institute of Technology)
Upadhyay, Akhil (Civil Engineering Department, Indian Institute of Technology)
Publication Information
Structural Engineering and Mechanics / v.52, no.1, 2014 , pp. 97-113 More about this Journal
Abstract
In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.
Keywords
artificial neural network; high performance concrete; prediction models; drying shrinkage; specific creep;
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