Doctoral Dissertations
Abstract
"Structural health monitoring is a challenging task that has recently received great attention from research communities. Due to its ability for predication and classification, neural network has become a promising tool for the identification of structural damages. In this dissertation, two backpropagation neural networks are introduced to predict the structural responses and to quantify the structural damages. The proposed methodology differs from many existing technologies in that it can be used to detect damages directly from the measured time signals without requiring modal characteristics. The methodology is developed with a benchmark highway bridge and is implemented for the Bill Emerson Memorial Cable-stayed Bridge. Extensive analyses indicated that the performance of an emulator neural network for response prediction is independent of its training sets after a novel prediction error indicator, response weighted root-mean-square (RW-RMS) was introduced. In comparison with the RMS error, the proposed RW-RMS is more suitable for damage detection since damage is typically associated with the peak response of a structure. As validated with the highway and the cable-stayed bridges, the parameter evaluator neural network can effectively quantify damages as small as a 10 percent reduction in flexural stiffness. To simulate the “healthy” structure and introduce various damage scenarios, a 3-dimensional FEM of the cable-stayed bridge is established and validated with the measured earthquake data. The first 22 natural frequencies of the bridge model agree with the measured frequencies by a less than 8 percent difference"--Abstract, page iii.
Advisor(s)
Chen, Genda
Committee Member(s)
LaBoube, Roger A.
Belarbi, Abdeldjelil
Ge, Yu-Ning (Louis)
Liu, Xiaoqing Frank
Department(s)
Civil, Architectural and Environmental Engineering
Degree Name
Ph. D. in Civil Engineering
Sponsor(s)
Missouri Transportation Institute
United States. Department of Transportation
Publisher
University of Missouri--Rolla
Publication Date
Spring 2007
Pagination
xii, 168 pages
Note about bibliography
Includes bibliographical references (pages 155-167).
Geographic Coverage
Cape Girardeau, Missouri
Rights
© 2007 Wenjian Wang, All rights reserved.
Document Type
Dissertation - Restricted Access
File Type
text
Language
English
Subject Headings
Cable-stayed bridges -- TestingNeural networks (Computer science)Structural analysis (Engineering) -- Mathematical models
Thesis Number
T 9196
Print OCLC #
180773759
Recommended Citation
Wang, Wenjian, "Structural condition assessment of the Bill Emerson Memorial Cable-Stayed Bridge using neural networks" (2007). Doctoral Dissertations. 1732.
https://scholarsmine.mst.edu/doctoral_dissertations/1732
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Comments
Financial support to complete this study was provided in part by the Missouri Transportation Institute (MTI) /MoDOT and by the U.S. Department of Transportation under the auspices of University Transportation Center (UTC) at the University of Missouri--Rolla. This support is gratefully acknowledged.