TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167398634&doi=10.1111%2fffe.14122&partnerID=40&md5=f6b95ded127e9668360afda22fd699cf
JF  - Fatigue and Fracture of Engineering Materials and Structures
A1  - Iqbal, M.
A1  - Karuppanan, S.
A1  - Perumal, V.
A1  - Ovinis, M.
A1  - Nouman, H.
VL  - 46
EP  - 4349
Y1  - 2023///
IS  - 11
N2  - The hotspot stress (HSS) approach for the fatigue design of tubular joints requires that peak HSS be known. Peak HSS in tubular joints is usually determined based on the stress concentration factor (SCF) estimated from empirical models developed through extensive experimental investigations and finite element analysis. While peak HSS usually occurs at a KT-joint's crown and saddle points, its location may change if the tubular joint is subjected to a combination of axial, in-plane bending, or out-of-plane bending loads. This study investigated the peak HSS and its location in a typical KT-joint subjected to the combined loading. Specifically, empirical models to determine the SCF around the brace axis have been developed using extensive finite element analysis and artificial neural networks (ANN) simulations. Less than 3 error was noticed between peak HSS determined through developed models and FEA. Hence, the ANN-based SCF equations and principle of superposition can be used to calculate peak HSS rapidly for fatigue design of tubular joints. This methodology is applicable for developing empirical models for SCF in other tubular joints and boundary conditions. © 2023 John Wiley & Sons Ltd.
N1  - cited By 4
KW  - Factor analysis; Fatigue of materials; Joints (structural components); Neural networks; Stress analysis; Stress concentration; Tubular steel structures
KW  -  Combined loading; Empirical model; Experimental investigations; Fatigue design; Finite element analyse; Hot-spot stress; Saddle point; Stress concentration factors; Tubular joints; Tubulars
KW  -  Finite element method
ID  - scholars18105
SP  - 4333
TI  - Empirical modeling of stress concentration factors using finite element analysis and artificial neural networks for the fatigue design of tubular KT-joints under combined loading
AV  - none
ER  -