(M.Tech) Structural Engineering, Dept. of Civil EngineeringAssistante Professor, Dept. of Civil Engineering Priyadharshini Institute of Tech & Science
This paper focuses on the procedure of statistical assessment of test results in reference to the strength
development of self compacting concrete and normally compacting concrete. A self compacting concrete and a normally
compacting concrete (NCC) with similar ultimate compressive strength were developed. The concrete cubes were tested at
7, 28, 60, 90, 120 and 150 days after normal water curing. For each case 10 samples were tested and the test results were
recorded for each sample on as obtained basis. To predict strength characteristics four input parameters namely water
cement ratio, aggregate cement ratio, percentage of fibers and aspect ratio were identified. The results of the present
investigation indicate that Genetic Algorithm based Artificial Neural Network (GANN) has strong potential as a feasible tool
for predicting strength characteristics of steel fibre reinforced concrete.
CH.BHAGYA LAKSHMI,G.SHINY PRIYANKA."Hybrid Neural Network Model for Compressive Strength of Reinforced Concrete". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 12,pp.1014-1019, December- 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1236.pdf,
: Genetic Algorithm , Back Propagation Network , Steel Fibre Reinforced Concrete , Neural Network .
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