摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据NewsRx记者从中华人民共和国南京发回的新闻报道，研究表明：“评估(TSSs)张弦桁架结构在关键构件失效情况下的抗连续倒塌能力是一个重大挑战，特别是当该指标在结构设计和性能评估过程中至关重要时。”机器学习(ML)方法可以在结构绩效评估过程中在输入和输出变量之间建立复杂和非线性的关系。本研究的资金来源包括国家重点研究开发项目、国家自然科学基金(NSFC)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating in Nanjing, Peo ple’s Republic of China, by NewsRx journalists, research stated, “Evaluating the progressive collapse resistance of truss string structures (TSSs) in the contex t of key member failure presents a significant challenge, particularly when this indicator is crucial during structural design and performance evaluation proces ses. Fortunately, machine learning (ML) methods can establish complex and nonlin ear relationships between input and output variables during structural performan ce evaluation.” Financial supporters for this research include National Key Research and Develop ment Program of China, National Natural Science Foundation of China (NSFC).
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