Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study applies multiple regression analysis and an artificial neural network in estimating the compressive strength of concrete that contains various amounts of blast furnace slag and fly ash, based on the properties of the additives (blast furnace slag and fly ash in this case) and values obtained by non-destructive testing rebound number and ultrasonic pulse velocity for 28 different concrete mixtures (M(control) and M(1)-M(27)) at different curing times (3, 7, 28, 90, and 180 days). The results obtained using the two methods are then compared and discussed. The results reveal that although multiple regression analysis was more accurate than artificial neural network in predicting the compressive strength using values obtained from non-destructive testing, the artificial neural network models performed better than did multiple regression analysis models. The application of an artificial neural network to the prediction of the compressive strength in admixture concrete of various curing times shows great potential in terms of inverse problems, and it is suitable for calculating nonlinear functional relationships, for which classical methods cannot be applied. (C) 2011 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Admixture concrete, Compressive strength, Multiple regression analysis, Artificial neural network
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
38
Sayı
8
