English  |  正體中文  |  简体中文  |  Items with full text/Total items : 16768/19061 (88%)
Visitors : 6016136      Online Users : 283
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/27889


    標題: Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches
    作者: Kao, Ching-Yun
    Loh, Chin-Hsiung
    貢獻者: 職業安全衛生系
    關鍵字: Artificial Neural Networks
    Structural Health Monitoring
    Long-Term Static Deformation Data
    Statistical Analysis
    Dam
    日期: 2013-03
    上傳時間: 2014-05-26 10:47:47 (UTC+8)
    出版者: Wiley-Blackwell
    摘要: The objective of this paper is to develop methods for extracting trends from long-term static deformation data of a dam and try to set an early warning threshold level on the basis of the results of analyses. The static deformation of a dam is mainly influenced by the water pressure (or water level) of the dam and the temperature distribution of the dam body. The relationship among the static deformation, the water level, and the temperature distribution of the dam body is complex and unknown; therefore, it can be approximated by static neural networks. Although the static deformation almost has no change during a very short time, it changes with time for long-term continuous observation. Therefore, long-term static deformation can be approximated dynamically using dynamic neural networks. Moreover, static deformation data is rich, but information is poor. Linear and nonlinear principal component analyses are particularly well suited to deal with this kind of problem. With these reasons, different approaches are applied to extract features of the long-term daily based static deformations of the Fei-Tsui arch dam (Taiwan). The methods include the static neural network, the dynamic neural network, principal component analysis, and nonlinear principal component analysis. Discussion of these methods is made. By using these methods, the residual deformation between the estimated and the recorded data are generated, and through statistical analysis, the threshold level of the static deformation of a dam can be determined on the basis of the normality assumption of the residual deformation. Copyright (C) 2011 John Wiley & Sons, Ltd.
    關聯: Structural Control & Health Monitoring, v.20 n.3, pp.282-303
    Appears in Collections:[職業安全衛生系(含防災所)] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML581View/Open


    All items in CNU IR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback