Comparison of artificial intelligence methods for predicting compressive strength of concrete
Yükleniyor...
Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Croatian Soc Civil Engineers-Hsgi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
artificial intelligence, regression, ANFIS, concrete compressive strength, multinational data, Self-Compacting Concrete, Elastic-Modulus, Silica Fume, Fly-Ash, Performance, Optimization, Machine, System, Anfis, Model
Kaynak
Gradevinar
WoS Q Değeri
Q4
Scopus Q Değeri
Q4
Cilt
73
Sayı
6
