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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The author would like to thank the anonymous reviewers and the editor for the constructive comments.
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Tutmez, B. A data-driven study for evaluating fineness of cement by various predictors. Int. J. Mach. Learn. & Cyber. 6, 501–510 (2015). https://doi.org/10.1007/s13042-014-0280-y
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DOI: https://doi.org/10.1007/s13042-014-0280-y