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Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine

G. Jayaprakash1, M. P. Muthuraj2,*

Assistant Professor, Dept. Civil Engg., KGISL Institute of Technology, Coimbatore-641 035, India.
Assistant Professor, Dept. Civil Engg., Coimbatore Institute of Technology, Coimbatore -641 014 India.

* Corresponding author: M. P. Muthuraj. Email: email.

Computers, Materials & Continua 2018, 54(1), 83-102. https://doi.org/10.3970/cmc.2018.054.083

Abstract

This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 75% of the data has been used for development of model and 30% of the data is used for validation. The predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature.

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Cite This Article

G. . Jayaprakash and M. P. . Muthuraj, "Prediction of compressive strength of various scc mixes using relevance vector machine," Computers, Materials & Continua, vol. 54, no.1, pp. 83–102, 2018.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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