首页|Researcher at Toyohashi University of Technology Publishes New Data on Machine L earning (Machine learning-based prediction of staticAlly equivalent seismic forc es in pin-supported cylindrical reticulated shells)
Researcher at Toyohashi University of Technology Publishes New Data on Machine L earning (Machine learning-based prediction of staticAlly equivalent seismic forc es in pin-supported cylindrical reticulated shells)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of Toyohashi, Japan, by NewsRx editors, research stated, "Reticulated shells exhibit complex vibrations during earthquakes, encompassing components in horizontal and vertical directions, and multiple vibration modes occur." Our news reporters obtained a quote from the research from Toyohashi University of Technology: "In particular, single-layer reticulated shells with a smAll dept h relative to their span exhibit many vibration modes, and the shapes of these m odes can vary depending on the geometry. The method for rapidly setting equivale nt static seismic forces remains unexplored. In response to the above background , this study proposes a novel approach for calculating the seismic forces on sin gle-layer reticulated shells using machine learning techniques. The shells in fo cus are pin-supported cylindrical reticulated shells, typicAlly for the roofs of gymnasiums used as evacuation facilities during severe earthquakes in Japan. Ma chine learning uses numerical analysis results for approximately 20,000 shells, with varied spans, half-open angles, and aspect ratios. A method for preprocessi ng the principal vibration modes as image data is proposed, after which the imag ed vibration modes are predicted from the shape parameters of the shell using a neural network."
Toyohashi University of TechnologyToyo hashiJapanAsiaCyborgsEmerging TechnologiesMachine Learning
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