Structural condition assessment of the Bill Emerson Memorial Cable-Stayed Bridge using neural networks

Author

Wenjian Wang

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

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.

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 -- Testing
Neural networks (Computer science)
Structural analysis (Engineering) -- Mathematical models

Thesis Number

T 9196

Print OCLC #

180773759

Link to Catalog Record

Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.

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