Comparison of artificial intelligence methods for predicting compressive strength of concrete
Loading...
Files
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Croatian Soc Civil Engineers-Hsgi
Access Rights
info:eu-repo/semantics/openAccess
Abstract
Compressive strength of concrete is an important parameter in concrete design. Accurate 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.
Description
Keywords
artificial intelligence, regression, ANFIS, concrete compressive strength, multinational data, Self-Compacting Concrete, Elastic-Modulus, Silica Fume, Fly-Ash, Performance, Optimization, Machine, System, Anfis, Model
Journal or Series
Gradevinar
WoS Q Value
Q4
Scopus Q Value
Q4
Volume
73
Issue
6
