摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的新研究结果已经发表。根据俄罗斯莫斯科的新闻报道，By NewsRx编辑Research说：“实际载荷识别是在(1)钢结构工程检验过程中解决的一个最重要的任务，(2)提高或恢复受损结构框架承载能力的系统设计，(3)结构健康监测，根据实际荷载值确定结构的应力应变状态(SSS)，实现各种工程目标。这项研究的资助者包括国家研究莫斯科国立Civil工程大学。本文引用了莫斯科国立土木工程大学国家研究中心的一句话：“载荷识别可能涉及一些不确定性，需要软计算技术。为此，本文提出了一种将结构力学、机器学习和人工神经网络的基本原理相结合的综合方法，该方法包括将结构分解为原语，利用机器学习数据进行设计。”和装配结构以对承受弹性应变的钢框架结构进行最终投影。最终投影用于识别沿杆长分布的点力和载荷参数。识别过程是检查(1)施加于单位载荷的重量系数矩阵和(2)使用最大LOAD值标准化的实际载荷之间的差异。神经网络训练和参数识别实例为简单的光束。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Moscow, Russia, b y NewsRx editors, research stated, "Actual load identification is a most importa nt task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged s tructural frames, and (3) structural health monitoring. Actual load values are u sed to determine the stress-strain state (SSS) of a structure and accomplish var ious engineering objectives." Funders for this research include National Research Moscow State University of C ivil Engineering. Our news correspondents obtained a quote from the research from National Researc h Moscow State University of Civil Engineering: "Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the a rticle presents an integrated method combining basic provisions of structural me chanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make proj ections, and assembling structures to make final projections for steel frame str uctures subjected to elastic strain. Final projections serve to identify paramet ers of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient m atrices applied to unit loads and (2) actual loads standardized using maximum lo ad values. Cases of neural network training and parameters identification are pr ovided for simple beams."
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