Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

Firat M., Gungor M.

ADVANCES IN ENGINEERING SOFTWARE, cilt.40, sa.8, ss.731-737, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 8
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.advengsoft.2008.12.001
  • Dergi Adı: ADVANCES IN ENGINEERING SOFTWARE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.731-737
  • Anahtar Kelimeler: Scour depth prediction, Circular bridge piers, Artificial Neural Networks, Generalized Regression Neural Networks, LOCAL SCOUR, FOUNDATION GEOMETRY, DESIGN METHOD, TIME, RUNOFF, SIMULATION, MODELS, SCALE
  • İnönü Üniversitesi Adresli: Hayır

Özet

In this study. Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers. (C) 2008 Elsevier Ltd. All rights reserved.