NDLI: Prediction of Slump in Concrete using Artificial Neural Networks

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Prediction of Slump in Concrete using Artificial Neural Networks

Content Provider	Semantic Scholar
Author	Agrawal, Vinay Sharma, A.
Copyright Year	2010
Abstract	High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and u iformity requirements that cannot be achieved routinely usin g conventional constituents and normal mixing, placing, and curing procedures. It is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed to show possible a pplicability of Neural Networks (NN) to predict the slump in High S trength Concrete (HSC). Neural Network models is constructe d, rained and tested using the available test data of 349 differe nt concrete mix designs of High Strength Concrete (HSC) gathered fr om a particular Ready Mix Concrete (RMC) batching plant. The most v ersatile Neural Network model is selected to predict the slu mp in concrete. The data used in the Neural Network models are arra nged in a format of eight input parameters that cover the Cement, Fl y Ash, Sand, Coarse Aggregate (10 mm), Coarse Aggregate (20 mm), Water, Super-Plasticizer and Water/Binder ratio. Furthermo re, to test the accuracy for predicting slump in concrete, the fina l selected model is further used to test the data of 40 different concr ete mix designs of High Strength Concrete (HSC) taken from the other b atching plant. The results are compared on the basis of error func tion (or performance function). Keywords—Artificial Neural Networks, Concrete, prediction of slump, slump in concrete
Starting Page	279
Ending Page	286
Page Count	8
File Format	PDF HTM / HTML
Volume Number	4
Alternate Webpage(s)	http://www.waset.org/journals/waset/v69/v70-5.pdf
Alternate Webpage(s)	http://waset.org/publications/8773/prediction-of-slump-in-concrete-using-artificial-neural-networks
Alternate Webpage(s)	http://www.waset.org/journals/waset/v69/v69-5.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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