首页|Researcher from National Research Moscow State University of Civil Engineering R eports Recent Findings in Machine Learning (Load Identification in Steel Structu ral Systems Using Machine Learning Elements: Uniform Length Loads and Point Forc es)
Researcher from National Research Moscow State University of Civil Engineering R eports Recent Findings in Machine Learning (Load Identification in Steel Structu ral Systems Using Machine Learning Elements: Uniform Length Loads and Point Forc es)
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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."
National Research Moscow State Universit y of Civil EngineeringMoscowRussiaEurasiaCyborgsEmerging TechnologiesEngineeringMachine Learning
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