Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks

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(1996). IIW-Fatigue design of welded joints and components: XIII-1539-96/XV-845-96, Paris, France." /> <meta name="citation_reference" content="citation_title=Experimental and Numerical Study of the Elastic SCF of Tubular Joints; citation_author=Mostafa Atteya; citation_author=Ove Mikkelsen; citation_author=John Wintle; citation_author=Gerhard Ersdal; citation_volume=14; citation_issue=15; citation_publication_date=2021/7/28; citation_pages=4220; citation_doi=10.3390/ma14154220" /> <meta name="citation_reference" content="citation_title=Stress concentration factors in FRP-reinforced tubular DKT joints under axial loads; citation_author=E. Zavvar; citation_author=J. Henneberg; citation_author=C. Guedes Soares; citation_volume=90; citation_publication_date=2023/7; citation_pages=103429; citation_doi=10.1016/j.marstruc.2023.103429" /> <meta name="citation_reference" content="citation_title=Stress concentration factors induced by out-of-plane bending loads in ring-stiffened tubular KT-joints of jacket structures; citation_author=Hamid Ahmadi; citation_author=Esmaeil Zavvar; citation_volume=91; citation_publication_date=2015/6; citation_pages=82-95; citation_doi=10.1016/j.tws.2015.02.011" /> <meta name="person" content="Mohsin Iqbal, Saravanan Karuppanan, Veeradasan Perumal, Mark Ovinis, Muhammad Faizan, Adnan Rasul, Muhammad Iqbal" /> <meta name="description" content="Stress concentration factor (SCF) is an important parameter for the fatigue design of offshore joints. There are many empirical equations for quick estimation of SCF in tubular joints, based on experimental and numerical investigations. However, most of these equations apply at the crown and saddle points only, even though the maximum SCF may not always occur at these points, resulting in overestimated fatigue life. As the maximum SCF location varies due to multiplanar loads, damage, or reinforcement of joints, its location and magnitude are critical for a realistic fatigue life estimation. However, conventional statistical tools cannot approximate the complex behavior of SCF around the brace axis. On the other hand, artificial neural networks (ANN) can efficiently approximate complex phenomena. This study uses ANN to develop empirical models for determining SCF around the weld toe of KT-joints subjected to in-plane bending (IPB) loads. Eighteen hundred and fifty-eight (1858) designs were simulated using finite element analyses to generate data for training the ANN. Two IPB load conditions were focused on, and empirical equations were proposed for SCF around the chord side of the central brace-chord interface. These equations approximate maximum SCF with less than 5% error. This methodology applies to other joints and load configurations also." /> <meta name="keywords" content="Artificial Neural Networks, Empirical Modeling, Hot-Spot Stress, In-Plane Bending Load, Stress Concentration Factor, Tubular Joints" /> <meta name="copyright" content="2024 Trans Tech Publications Ltd. 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class="page-name-block underline-begin"> <h1 class="page-name-block-text">Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks</h1> </div> <div class="paper-statistics"> <div class="loading"> <i class="inline-icon download-and-visitor-statistics-icon"></i> <span class="normal-text" id="paperDownloadsAndVisitorsCount"></span> </div> </div> <div class="clearfix"></div> <div class="page-paper-title"> <div class="preview-block"> <img alt="Article Preview" width="128" height="180" src="/JERA.71.79/thumbnail.gif"> <div id="preview-button" data-url-preview-log="/Paper/PreviewImageLog?paperId=605756"> <i class="inline-icon preview-icon"></i> </div>  <div id="paper-preview-modal" class="modal fade"> <div class="modal-dialog" role="document"> <div class="popup-page-name underline-begin"> <div class="page-name-block-text">Article Preview</div> </div> <img alt="Article Preview" class="preview-image lazyload" data-src="/JERA.71.79/preview.gif"> <a data-dismiss="modal" title="Close" class="inline-icon close-icon"></a> </div> </div>  </div> <div class="abstract-block-description"> <h3 class="page-paper-first-header">Abstract:</h3> <p class="normal-text"> Stress concentration factor (SCF) is an important parameter for the fatigue design of offshore joints. There are many empirical equations for quick estimation of SCF in tubular joints, based on experimental and numerical investigations. However, most of these equations apply at the crown and saddle points only, even though the maximum SCF may not always occur at these points, resulting in overestimated fatigue life. As the maximum SCF location varies due to multiplanar loads, damage, or reinforcement of joints, its location and magnitude are critical for a realistic fatigue life estimation. However, conventional statistical tools cannot approximate the complex behavior of SCF around the brace axis. On the other hand, artificial neural networks (ANN) can efficiently approximate complex phenomena. This study uses ANN to develop empirical models for determining SCF around the weld toe of KT-joints subjected to in-plane bending (IPB) loads. Eighteen hundred and fifty-eight (1858) designs were simulated using finite element analyses to generate data for training the ANN. Two IPB load conditions were focused on, and empirical equations were proposed for SCF around the chord side of the central brace-chord interface. These equations approximate maximum SCF with less than 5% error. 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Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks | Scientific.Net