4.5 Article

Multi-block combined diagnosis indexes based on dam block comprehensive displacement of concrete dams

期刊

OPTIK
卷 129, 期 -, 页码 172-182

出版社

ELSEVIER GMBH, URBAN & FISCHER VERLAG
DOI: 10.1016/j.ijleo.2016.10.040

关键词

Dam displacement; Hybrid model; Comprehensive block displacement; Multi-block combined diagnosis index

类别

资金

  1. National Natural Science Foundation of China [51139001, 41323001, 51479054, 51579086, 51379068, 51579083, 51279052, 51579085]
  2. Jiangsu Natural Science Foundation [BK20140039]
  3. Research Fund for the Doctoral Program of Higher Education of China [20130094110010]
  4. Priority Academic Program Development of Jiangsu Higher Education Institutions [YS11001]
  5. Jiangsu Province Six Talent Peaks Project [JY-008, JY-003]
  6. Central University Basic Research Project [2015B20714]

向作者/读者索取更多资源

Timely diagnosis of the service condition of concrete dam is required to prevent the potential great loss of life and property in case of dam failure. Scientific and reliable diagnosis indexes are significant for effective dam diagnosis. Traditional dam diagnosis focused on displacement at a single point. Rational combined diagnosis based on multi-point displacement is rare. This paper presents a multi-block combined diagnosis method for concrete dam displacement. This method combines comprehensive block displacement and multidimensional confidence region method. The hybrid model, together with structure analysis, is selected to perform prediction. Comprehensive block displacement, a displacement system made up of single-point displacements with different weights, is introduced to describe block displacement. The weights are determined with improved projection pursuit method, which is adept in analyzing and processing high-dimensional data. Multi-block combined diagnosis indexes are then established in multidimensional space based on the residual sequences, and the obtained ellipse diagram can be a straightforward and visualized tool for dam diagnosis. The multi-block combined diagnosis method is recommended because of its high accuracy and reliability. (C) 2016 Elsevier GmbH. All rights reserved.

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