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Published: Građevinar 73 (2021) 6
Paper type: Scientific research paper
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Comparison of artificial intelligence methods for predicting compressive strength of concrete

Mehmet Timur Cihan

Abstract

prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.

Keywords
artificial intelligence, Regression, ANFIS, Concrete compressive strength, multinational data

HOW TO CITE THIS ARTICLE:

Cihan, M. T.: Comparison of artificial intelligence methods for predicting compressive strength of concrete, GRAĐEVINAR, 73 (2021) 6, pp. 617-632, doi: https://doi.org/10.14256/JCE.3066.2020

OR:

Cihan, M. T. (2021). Comparison of artificial intelligence methods for predicting compressive strength of concrete, GRAĐEVINAR, 73 (6), 617-632, doi: https://doi.org/10.14256/JCE.3066.2020

LICENCE:

Creative Commons License
This paper is licensed under a Creative Commons Attribution 4.0 International License.
Authors:
Timur web
Mehmet Timur Cihan
Tekirdağ Namık Kemal University, Turkey
Çorlu Faculty of Engineering
Department of Civil Engineering