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

Translated title of the contribution: A Forecast of Building Destruction in Earthquakes: Applications of Artificial Neural Network

Ko-Wan Tsou, 張 益三, 杜 建宏

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Translated title of the contributionA Forecast of Building Destruction in Earthquakes: Applications of Artificial Neural Network
Original languageChinese (Traditional)
Pages (from-to)21-41
Journal住宅學報
Volume15
Issue number1
Publication statusPublished - 2006 Jun

All Science Journal Classification (ASJC) codes

  • Archaeology

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