[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127672-en":3,"doc-seo-127672-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127672,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings - - design model","The study addresses web-post buckling (WPB) in high-strength steel (HSS) beams featuring periodical elliptically-based web (EBW) openings, which are attractive for their high strength-to-weight ratio and reduced floor height. It develops and validates a finite element numerical model to generate 10,764 WPB data cases across S460, S690, and S960 steel grades. Machine learning techniques—ANN, SVR, and GEP—are trained and tested, and new ML-based design models are proposed and compared with analytical methods.","Results in Engineering 21 (2024) 101749  \nContents lists available at ScienceDirect  \nResults in Engineering  \njournal [homepage:](homepage: www.sciencedirect.com/journal/results-in-engineering)[ www.sciencedirect.com/journal/results-in-engineering](homepage: www.sciencedirect.com/journal/results-in-engineering)  \n| Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings\u003Cbr>Musab Rabia, Yazeed S. Jweihan b, Ikram Abarkanc, Felipe Piana Vendramell Ferreira d, Rabee Shamasse, Vireen Limbachiyaf, *, Konstantinos Daniel Tsavdaridisg,\u003Cbr>Luis Fernando Pinho Santos f\u003Cbr>a Dept of Civil Engineering, Jerash University, Jerash, 26150, Jordan\u003Cbr>b Civil and Environmental Engineering Department, College of Engineering, Mutah University, Mutah, P.O. BOX 7, Karak, 61710, Jordan c Department of Physics, Faculty of Sciences, Abdelmalek Essaˆadi University, 93002 Tetouan, Morocco\u003Cbr>d Faculty of Civil Engineering, Federal University of Uberlˆandia,– Campus Santa Mˆonica, Uberlˆandia, Minas Gerais, Brazil e Department of Civil and Environmental Engineering, Brunel University London, London, UK\u003Cbr>f Division of Civil and Building Services Engineering, School of Build Environment and Architecture, London South Bank University, UK g Department of Engineering, School of Science and Technology, City, University of London, Northampton Square, EC1V 0HB, London, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Finite element modelling\u003Cbr>Web-post buckling resistance Elliptically-based web openings High strength steel beams Artificial neural network\u003Cbr>Gene expression programming Support vector machine regression |  | The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown tobe powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings. |\n\n1. Introduction  \nSteel beams with periodical web openings (i.e., castellated, cellular and Angelinas) can be used in multi-story building designs, since they have many advantages such as the reduction in the structure’s selfweight and the floor height since as a result of allowing different utility services to pass through the web openings [1,2]. The present study focuses on S460, S690, S960 grade high strength steel beams (HSS) beams comprising periodical elliptically-based web (EBW) openings. The manufacturing and castellation process include three main steps: thermal cutting, shifting and welding [3]. These procedures result in an  \nincrease in inertia about the strong axis leading to","cbCaigh5eTBoIB2N","https://ap.wps.com/l/cbCaigh5eTBoIB2N","pdf",8383327,1,15,"English","en",105,"# Introduction\n## Web openings and buckling modes\n## Focus on WPB resistance in HSS beams\n# Proposed approach and methodology\n## Numerical model and dataset generation\n## Machine learning algorithms and model development\n## Design-model formulation and comparisons","[{\"question\":\"Why are elliptically-based web (EBW) openings important in high-strength steel beams?\",\"answer\":\"They improve structural efficiency by offering a high strength-to-weight ratio and reducing floor height by allowing utilities to pass through web openings.\"},{\"question\":\"What failure mode does the paper focus on?\",\"answer\":\"The paper focuses on web-post buckling (WPB), a local failure mode associated with S-shaped double curvature caused by horizontal shear stresses in the web-post region.\"},{\"question\":\"How is the machine learning training data generated?\",\"answer\":\"A validated numerical finite element model is used to conduct 10,764 web-post finite element models covering S460, S690, and S960 steel grades.\"}]","Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings - 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