Open AccessMathematical Model for Strength Prediction of Concrete under Influence of GGBS and Fly Ash
Author(s) -
Krishan kumar saini*,
Tarun Gehlot,
Dr Suresh Singh Sankhla
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6834.018520
Subject(s) - compressive strength , fly ash , ground granulated blast furnace slag , aggregate (composite) , correlation coefficient , geotechnical engineering , mathematics , structural engineering , computer science , materials science , engineering , statistics , composite material
There are many variables of concrete that affect its strength gaining characteristics. This study is a research to use the early compressive strength test result to evaluate compressive strength at different ages. Proper use of the early day compressive strength result to predict characteristic strength of normal weight concrete has been investigated. A simple model capable of predicting the compressive strength of concrete at any age is proposed for locally available aggregate concrete. The model develops a rational polynomial equation having only two coefficients. This study also proposes a simple justified relationship between the coefficient (strength at infinite time) with the strength values of concrete of a particular day. This relation almost make simple to understand and reliable to any the concrete strength prediction model. The developed model is validated for commonly used for local aggregate concrete. Data used in this study are collect from some previous studies of research scholars and recent experimental works of us at RCC lab of MBM Engineering College Jodhpur. along with data sets of conventional concrete (CC), we have also considered influence and variation of GGBS and FLYASH in Conventional concrete and we have selected the various data sets for M1 ( GGBS & FLY ASH ),M2( GGBS ) & M3( FLY ASH) member group .The research carried with the model using different data exhibit reliable prediction of concrete strength at different ages (7, 14, 28 days.) with good accuracy.