Empirical modeling of stress concentration factors using artificial neural networks for fatigue design of tubular T-joint under in-plane and out-of-Plane bending moments

Rasul, Adnan and Karuppanan, Saravanan and Perumal, Veeradasan and Ovinis, Mark and Iqbal, Mohsin and Alam, Khurshid (2024) Empirical modeling of stress concentration factors using artificial neural networks for fatigue design of tubular T-joint under in-plane and out-of-Plane bending moments. International Journal of Structural Integrity. ISSN 1757-9864

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Stress concentration factors (SCFs) are commonly used to assess the fatigue life of tubular T-joints in offshore structures. SCFs are usually estimated from parametric equations derived from experimental data and finite element analysis (FEA). However, these equations provide the SCF at the crown and saddle points of tubular T-joints only, while peak SCF might occur anywhere along the brace. Using the SCF at the crown and saddle can lead to inaccurate hotspot stress and fatigue life estimates. There are no equations available for calculating the SCF along the T-joint's brace axis under in-plane and out-of-plane bending moments.

In this work, parametric equations for estimating SCFs are developed based on the training weights and biases of an artificial neural network (ANN), as ANNs are capable of representing complex correlations. 1,250 finite element simulations for tubular T-joints with varying dimensions subjected to in-plane bending moments and out-of-plane bending moments were conducted to obtain the corresponding SCFs for training the ANN.

The ANN was subsequently used to obtain equations to calculate the SCFs based on dimensionless parameters (α, β, γ and τ). The equations can predict the SCF around the T-joint's brace axis with an error of less than 8% and a root mean square error (RMSE) of less than 0.05.

Accurate SCF estimation for determining the fatigue life of offshore structures reduces the risks associated with fatigue failure while ensuring their durability and dependability. The current study provides a systematic approach for calculating the stress distribution at the weld toe and SCF in T-joints using FEA and ANN, as ANNs are better at approximating complex phenomena than typical data fitting techniques. Having a database of parametric equations enables fast estimation of SCFs, as opposed to costly testing and time-consuming FEA.

Item Type: Article
Identification Number: https://doi.org/10.1108/IJSI-03-2024-0043
1 June 2024Accepted
14 June 2024Published Online
Uncontrolled Keywords: T-joint, artificial neural network, stress concentration factor, fatigue design, finite element analysis, in-plane bending, out-of-plane bending
Subjects: CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering
Divisions: Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User: Gemma Tonks
Date Deposited: 02 Jul 2024 13:31
Last Modified: 02 Jul 2024 13:31
URI: https://www.open-access.bcu.ac.uk/id/eprint/15627

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