Proceedings of the 1st International Conference on Sustainable Engineering Development and Technological Innovation, ICSEDTI 2022, 11-13 October 2022, Tanjungpinang, Indonesia

Research Article

Stress Concentration Factor Estimation of a Multi-planar DKT Tubular Joint through Finite Element and Machine Learning Approaches

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  • @INPROCEEDINGS{10.4108/eai.11-10-2022.2326418,
        author={Rudi Walujo Prastianto and Fulgentius Agiel Admiral and Yoyok Setyo Hadiwidodo and Daniel Mohammad Rosyid and Yeyes Mulyadi and Kriyo Sambodho},
        title={Stress Concentration Factor Estimation of a Multi-planar DKT Tubular Joint through Finite Element and Machine Learning Approaches},
        proceedings={Proceedings of the 1st International Conference on Sustainable Engineering Development and Technological Innovation, ICSEDTI 2022, 11-13 October 2022, Tanjungpinang, Indonesia},
        publisher={EAI},
        proceedings_a={ICSEDTI},
        year={2023},
        month={1},
        keywords={multiplanar dkt tubular joint finite element method machine learning regression stress concentration factor},
        doi={10.4108/eai.11-10-2022.2326418}
    }
    
  • Rudi Walujo Prastianto
    Fulgentius Agiel Admiral
    Yoyok Setyo Hadiwidodo
    Daniel Mohammad Rosyid
    Yeyes Mulyadi
    Kriyo Sambodho
    Year: 2023
    Stress Concentration Factor Estimation of a Multi-planar DKT Tubular Joint through Finite Element and Machine Learning Approaches
    ICSEDTI
    EAI
    DOI: 10.4108/eai.11-10-2022.2326418
Rudi Walujo Prastianto1,*, Fulgentius Agiel Admiral1, Yoyok Setyo Hadiwidodo1, Daniel Mohammad Rosyid1, Yeyes Mulyadi1, Kriyo Sambodho1
  • 1: Department of Ocean Engineering ITS, Surabaya
*Contact email: rudiwp@oe.its.ac.id

Abstract

The Double KT (DKT) multi-planar tubular joint is frequently found in the jacket offshore structures. An important aspect in designing the multi-planar tubular joint is accuracy in predicting the Stress Concentration Factor (SCF). Despite its practicability with considerable accuracy on stress analysis and estimating the SCF, the Finite Element (FE) method needs high computational time and effort. Therefore, this study will develop alternative SCF equations for DKT tubular joint with regression analysis as one of the machine learning techniques to increase equation’s accuracy while reducing computational time. The variation of the DKT tubular joint was determined based on the validity range of the geometric parameters of the tubular joint (β, τ , and γ) and the combination of axial, in-plane bending (IPB), and out-plane bending (OPB) moment loadings. Stress distribution and concentration factor of the DKT joints were analyzed based on FE model of the joint. Then, the SCF results from the FE analysis were used in the regression analysis to obtain new equations. Six SCF equations including for both brace-side and chord-side have been obtained while the reliability of the equation has been checked using Acceptance Criteria based on UK Department of Energy and showed good results.