首页|Research from Politehnica University of Bucharest in Machine Learning Provides N ew Insights (Innovative approach to estimate structural damage using linear regr ession and K-nearest neighbors machine learning algorithms)
Research from Politehnica University of Bucharest in Machine Learning Provides N ew Insights (Innovative approach to estimate structural damage using linear regr ession and K-nearest neighbors machine learning algorithms)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Buchares t, Romania, by NewsRx correspondents, research stated, “Conventional structural design methodologies often utilize elastic analysis techniques, such as the equi valent static force method and the response spectrum method.” Financial supporters for this research include National University of Science An d Technology; Aosr. Our news editors obtained a quote from the research from Politehnica University of Bucharest: “While these methods are known for their simplicity and computatio nal efficiency, they prove inadequate in capturing the extent of structural dama ge caused by seismic forces. Additionally, employing nonlinear dynamic analysis to estimate structural damage represents a challenging and intricate task, posin g difficulties for many structural designers. Consequently, the objective of thi s paper is to present an innovative methodology for evaluating seismic structura l damage of moment-resisting frame structures. This involves the utilization of machine learning algorithms, which have been trained and tested on a large data set generated using a newly developed and numerically efficient simulation proce dure.”
Politehnica University of BucharestBuc harestRomaniaEuropeAlgorithmsCyborgsEmerging TechnologiesK-nearest N eighborMachine Learning
NETL
NSTL
