Comparison of artificial intelligence methods for predicting compressive strength of concrete

Građevinar

article

Mehmet Timur Cihan¹ 

[1] Tekirdağ Namık Kemal University, Turkey Çorlu Faculty of Engineering Department of Civil Engineering

关键词: artificial intelligence;    Regression;    ANFIS;    Concrete compressive strength;    multinational data;   

DOI  :  10.14256/JCE.3066.2020

学科分类：社会科学、人文和艺术（综合）

来源: Hrvatsko Drustvo Gradevinskih Inzenjera

【 摘 要 】

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.

【 授权许可】

CC BY