摘要
一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者从英国拉夫堡发回的消息，研究表明：“本文旨在建立一种基于机器学习的(ML)算法，来预测典型CFS护套墙板中广泛使用的带约束法兰的CO LD型钢(CFS)槽钢的屈曲抗力。”首次研究了截面约束对CFS渠道在纯轴压荷载和弯矩作用下弹性屈曲性能的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news originating from Loughborough, United Kingdom, by NewsRx correspondents, research stated, “This paper aims to d evelop Machine Learning (ML) algorithms to predict the buckling resistance of co ld -formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckli ng behaviour of CFS channels subjected to pure axial compressive load or bending moment.”
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