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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

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Hybrid Neural Network Model for Compressive Strength of Reinforced Concrete

(M.Tech) Structural Engineering, Dept. of Civil Engineering
Assistante 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 :,

Keywords : Genetic Algorithm , Back Propagation Network , Steel Fibre Reinforced Concrete , Neural Network .

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