Studies Conducted at University of South Africa on Machine Learning Recently Rep orted (Explainable Machine Learning Models for Predicting the Ultimate Bending C apacity of Slotted Perforated Cold-formed Steel Beams Under Distortional Bucklin g)

南非大学最近发表的机器学习研究报告(可解释的机器学习模型预测开缝多孔冷弯薄壁型钢梁在变形屈曲下的极限弯曲能力)

Robotics & Machine Learning Daily News2024，Issue(Dec.4) ：16-17.

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Studies Conducted at University of South Africa on Machine Learning Recently Rep orted (Explainable Machine Learning Models for Predicting the Ultimate Bending C apacity of Slotted Perforated Cold-formed Steel Beams Under Distortional Bucklin g)

南非大学最近发表的机器学习研究报告(可解释的机器学习模型预测开缝多孔冷弯薄壁型钢梁在变形屈曲下的极限弯曲能力)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。据新闻报道NewsRx编辑从南非约翰内斯堡发回的报道称，“这项研究发展了”可解释的马赫数学习(ML)模型预测冷弯薄壁型钢极限抗弯能力具有交错开缝孔洞的(CFS)梁，重点研究畸变屈曲行为。利用CFS唇形通道432非线性有限元分析模拟数据集，10ml算法，包括四个基本模型和六个集合模型，被评估。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are pre sented in a new report. According to newsreporting out of Johannesburg, South A frica, by NewsRx editors, research stated, “This study developsexplainable mach ine learning (ML) models to predict the ultimate bending capacity of cold-formed steel(CFS) beams with staggered slotted perforations, focusing on distortional buckling behavior. Utilizing adataset from 432 non-linear finite element analy sis simulations of CFS Lipped channels, ten ML algorithms,including four basic and six ensemble models, were evaluated.”

Key words

Johannesburg/South Africa/Africa/Cybo rgs/Emerging Technologies/Machine Learning/University of South Africa

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出版年

2024

Robotics & Machine Learning Daily News

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