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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2015

01-06-2015 | Original Article

A data-driven study for evaluating fineness of cement by various predictors

Author: Bulent Tutmez

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2015

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Abstract

Modelling relationships among cement and concrete parameters from different perspectives is preferred due to its practical importance. The relationship between chemical ingredients and specific surface area which addresses fineness of cement were appraised via three predictors: robust regression (RR), support vector regression (SVR) and multi-layer perception (MLP). The main motivation of the study was to give a comparative assessment with sparse data based on accuracy of the models. In addition to accuracy, smoothing level of the estimations was also considered and the performances of three models were compared with the former practices. The experimental studies showed that the SVR model performs better than the rest of the models for identifying the relationships. The potentials of the MLP and the RR models have also been discussed.
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Metadata
Title
A data-driven study for evaluating fineness of cement by various predictors
Author
Bulent Tutmez
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0280-y

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