Prediction of Joint Shear Strain–Stress Envelope Through Generalized Regression Neural Networks

YILMAZ M. O., BEKİROĞLU S.

Arabian Journal for Science and Engineering, cilt.46, sa.11, ss.10819-10833, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 11
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s13369-021-05565-z
  • Dergi Adı: Arabian Journal for Science and Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, Pollution Abstracts, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.10819-10833
  • Anahtar Kelimeler: RC joints, Artificial neural networks, Generalized regression neural networks, Shear strength
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

© 2021, King Fahd University of Petroleum & Minerals.In structural engineering practice, it is widely accepted that beam-to-column joints in reinforced concrete frames can be idealized as rigid regions. However, recent studies demonstrated that severe damage can be observed in these regions and neglecting inelastic deformations can lead to misinterpretations in performance-based seismic design and assessment process. Despite the large experimental and analytical efforts in establishing a generalized method for predicting inelastic behavior of exterior and interior beam-to-column joints, literature survey revealed that there has only been little consensus about the factors affecting the shear stress–strain envelope. This study introduces the application of Generalized Regression Neural Networks to joint deformation problem and proposes a prediction model. Accuracy and reliability of the proposed model are demonstrated with statistical measures and comparison to various methods available in the literature.