Stress Concentration Factors in KT-Joints Subjected to Complex Bending Loads Using Artificial Neural Networks
Publish place: Civil Engineering Journal، Vol: 10، Issue: 4
Publish Year: 1403
Type: Journal paper
Language: English
View: 95
This Paper With 18 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
Export:
Document National Code:
JR_CEJ-10-4_004
Index date: 21 May 2024
Stress Concentration Factors in KT-Joints Subjected to Complex Bending Loads Using Artificial Neural Networks abstract
Fatigue analysis of tubular joints based on peak stress concentration factor (SCF) is critical for offshore structures as it determines the fatigue life of the joint and possibly the overall structure. It is known that peak SCF occurs at the crown position for in-plane bending (IPB) and at the saddle position for out-of-plane bending (OPB). Tubular joints of offshore structures are under multiplanar bending, comprising IPB and OPB. When a joint is subjected to IPB and OPB loads simultaneously, the peak SCF occurs somewhere between the crown and the saddle. However, existing equations estimate SCF at the crown and saddle only when a joint is subjected to IPB or OPB. It was found that the position and magnitude of peak SCF under simultaneous IPB and OPB depend on the relative magnitudes of these uniplanar load components. The crown and saddle position SCF can be substantially lower than the cumulative peak SCF. Empirical models are proposed for computing peak SCF for KT-joints subjected to multiplanar bending. These models were developed through regression analysis using artificial neural networks (ANN). The ANN training data was generated through 3716 ANSYS finite element simulations. The empirical model was validated using models available in the literature and can determine peak SCF with an error of less than 1.5%. Doi: 10.28991/CEJ-2024-010-04-04 Full Text: PDF
Stress Concentration Factors in KT-Joints Subjected to Complex Bending Loads Using Artificial Neural Networks Keywords:
Stress Concentration Factors in KT-Joints Subjected to Complex Bending Loads Using Artificial Neural Networks authors
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :