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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 10, 2012.
Abstract: Self-compacting concrete is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction even in congested reinforcement without segregation and bleeding. In the present study self compacting concrete mixes were developed using blend of fly ash and rice husk ash. Fresh properties of theses mixes were tested by using standards recommended by EFNARC (European Federation for Specialist Construction Chemicals and Concrete system). Compressive strength at 28 days was obtained for these mixes. This paper presents development of Adaptive Neuro-fuzzy Inference System (ANFIS) model for predicting compressive strength of self compacting concrete using fly ash and rice husk ash. The input parameters used for model are cement, fly ash, rice husk ash and water content. Output parameter is compressive strength at 28 days. The results show that the implemented model is good at predicting compressive strength.
S. S, Pathak, Dr. Sanjay Sharma, Dr. Hemant Sood and Dr. R. K. Khitoliya, “Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System” International Journal of Advanced Computer Science and Applications(IJACSA), 3(10), 2012. http://dx.doi.org/10.14569/IJACSA.2012.031020
@article{Pathak2012,
title = {Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.031020},
url = {http://dx.doi.org/10.14569/IJACSA.2012.031020},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {10},
author = {S. S, Pathak and Dr. Sanjay Sharma and Dr. Hemant Sood and Dr. R. K. Khitoliya}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.