Artificial neural network for predicting the flexura bond strength of FRP bars in concrete
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Walter De Gruyter Gmbh
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The bond strength between fibre-reinforced polymer (FRP) rebars and concrete is one of the most significant aspects of composite behaviour for rebars and concrete. In this study, a database of 408 beam type specimens consisting of beam end specimens, beam anchorage specimens and splice beam specimens was compiled from the current literature and used to develop a simple prediction using the artificial neural network (ANN). The data used for modelling were organised in a format of eight input parameters that include FRP type, cover bar surface, confinement, bar diameter (d(b)), concrete compressive strength (root f(c)), minimum cover-to-bar-diameter ratio (c/d(b)), bar-development-length-to-bar-diameter ratio (l/d(b)), and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameters (A(tr)/snd(b)). Additionally, a simple prediction formula by regression analysis was developed. The root mean square error and R-2 values of the testing data were found in order to compare the results of both ANN and the proposed model with existing regulations. The new ANN model predicts the bond strength of FRP bars in reinforced concrete with 0.8989 R-2, thus yielding better results when compared with existing regulations.
Açıklama
Anahtar Kelimeler
Ann, Bond Strength, Frp Bars
Kaynak
Science And Engineering Of Composite Materials
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
26
Sayı
1
