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Strength predictions of GGBS based cement mortar with different M-Sands using Neural networks
A.B. Kumar, ,
Published in IOP Publishing Ltd
Volume: 1716
Issue: 1
This study presents the compressive strength predictions of Ground Granulated Blast Furnace Slag (GGBS) based cement mortars containing two different types of M Sands (normal M sand and white M sand) which have been replaced effectively with the ordinary Portland cement. The defined mix ratios of mortar cubes are examined for compressive strength at 7, 14 and 28 days. Artificial Neural Network is a useful tool to predict various data's strengths, making the work much more comfortable. Then the obtained compressive strength results of GGBS based cement based mortars varied with two types of M-sands at different days were feed into ANN tool box in MATLAB software to acquire the strength predictions. Experimental results indicated that the compressive strength results of GGBS based mortar with white M-sand showed superior results than the normal M sand. The predicted compressive strength results of GGBS based cement mortars obtained from ANN framework was in good agreement with the experimental results. © 2021 Institute of Physics Publishing. All rights reserved.
About the journal
JournalData powered by TypesetJournal of Physics: Conference Series
PublisherData powered by TypesetIOP Publishing Ltd