Analytical Method Of Fibre Reinforced Self Compacting Concrete Using Artificial Neural Network

Authors

  • Johnsirani K S,

Abstract

Self compacting concrete (SCC) is a highly flowable type of concrete that spreads into form without using mechanical vibration. This paper presents an experimental and numerical investigation on Self Compacting Concrete (SCC) and fiber reinforced self compacting concrete  (FRSCC) with various partial replacements of fly ash, silica fume and combination of both fly ash and silica fume using quarry dust as fine aggregate for polypropylene fiber reinforced self compacting concrete beam. In this study two methodologies which have been applied on same data of SCC mixtures, using artificial neural network (ANN) and General. Regression Neural Network (GRNN) Moreover the structural performance of Self Compacting Reinforced concrete (SCRC) and Self CompactingFibre Reinforced concrete (SCFRC) were studied and compared with analytical method of Artificial Neural Network. and General. Regression Neural Network (GRNN) The method adopted for modeling the concrete beams using three-dimensional finite element technique was proved to be reliable predictive tool for the analysis of concrete beams. However, it is very important to choose appropriate finite elements and correct mesh density to obtain satisfactory solution to the problem, while carrying out non-linear finite element analysis. Also, It has been found that the multivariate linear regression can make a reasonable prediction of structural performance characteristics of FRSCC. A close agreement has been obtained between the predicted and experimental results.

Published

2020-11-01

Issue

Section

Articles