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タイトル
和文:Development of a damage evaluation tool for non-instrumented buildings using machine learning 
英文:Development of a damage evaluation tool for non-instrumented buildings using machine learning 
著者
和文: Taipicuri Huacre Yenifer Carol, Mori Miki, 楠浩一, Zavala Carlos, 毎田悠承, Diaz Miguel, YEOW Trevor Zhiqing.  
英文: Yenifer Carol Taipicuri Huacre, Miki Mori, Koichi Kusunoki, Carlos Zavala, Yusuke Maida, Miguel Diaz, Trevor Zhiqing Yeow.  
言語 English 
掲載誌/書名
和文: 
英文: 
巻, 号, ページ        
出版年月 2022年12月 
出版者
和文: 
英文: 
会議名称
和文:日本地震工学会・大会-2022 
英文:JAEE Annual Meeting 2022 
開催地
和文:北海道 
英文:Hokkaido 
公式リンク https://www.jaee.gr.jp/jp/wp-content/uploads/2022/11/program.pdf
 
アブストラクト Visual building damage inspections conducted after strong earthquakes can take weeks to months to be completed. While methods that use floor acceleration data to estimate damage are available, only a small portion of buildings are instrumented. To achieve rapid damage assessments over a wide area, methods to estimate damage to non-instrumented buildings are required. This study details ongoing research to estimate the response of non-instrumented buildings as an intermediary step to develop a regional-level building damage assessment.

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