建物震害毀損度預測模式之研究-倒傳遞類神經網路法之應用

Ko-Wan Tsou, 張 益三, 杜 建宏

研究成果: Article同行評審

摘要

This research investigates potential variables of buding destruction in earthquakes and their
influences by conducting an empirical study of Jwu-Shan area in Taiwan. Jwu-Shan was one
of the most serious damaged areas during the 921 earthquake which was 7.0 magnitude. In this
study , MATLAB6.5 software of back-propagation neural network was used with its superior
attributes, i.e., learning and memory, to establish a forecast model of hazards in middle and
lower buildings in an earthquake by means of training, testing and validation. The model was
tested with the data of partial old communities in Chia-Yi. Damaged buildings were classified
into 3 categories: safe, unsafe, and collapse by geography information system (GIS) with data
spatialization and transformation. The results suggest that the artificial neural network is capable
to forecast building destruction in earthquakes with a low error rate. The paper concludes with
applications of a back-propagation neural network in planning urban disaster prevention.
貢獻的翻譯標題A Forecast of Building Destruction in Earthquakes: Applications of Artificial Neural Network
原文???core.languages.zh_TW???
頁(從 - 到)21-41
期刊住宅學報
15
發行號1
出版狀態Published - 2006 六月

All Science Journal Classification (ASJC) codes

  • 考古學

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