首页|Study Data from Jerash University Provide New Insights into Machine Learning (Ma chine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings)
Study Data from Jerash University Provide New Insights into Machine Learning (Ma chine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of Jerash, Jordan, by New sRx editors, research stated, "The use of periodical elliptically-based web (EBW ) openings in high strength steel (HSS) beams has been increasingly popular in r ecent years mainly because of the high strength-to-weight ratio and the reductio n in the floor height as a result of allowing different utility services to pass through the web openings." Our news editors obtained a quote from the research from Jerash University: "How ever, 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 p rediction of the web-post buckling capacity. Therefore, the present paper aims t o 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 dev eloped and validated with the aim of conducting a total of 10,764 web-post finit e element models, considering S460, S690 and S960 steel grades. This data is emp loyed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Prog ramming (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."
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