Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks
Küçük Resim Yok
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Düzce Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, the present serviceability index of rigid highway pavements has been predicted by an artificial neural network (ANN) model. For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching. The developed ANN model has a higher regression value than the AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Pavement, Rigid Pavements, Serviceability
Kaynak
İleri Teknoloji Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
2
Sayı
1
