Structural condition assessment of the Bill Emerson Memorial Cable-Stayed Bridge using neural networks
"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"--Abstract, leaf iii.
LaBoube, Roger A.
Ge, Yu-Ning (Louis)
Liu, Xiaoqing Frank
Civil, Architectural and Environmental Engineering
Ph. D. in Civil Engineering
Missouri Transportation Institute
United States. Department of Transportation
University of Missouri--Rolla
xii, 168 leaves
Bill Emerson Memorial Bridge (East Cape Girardeau, Ill., and Cape Girardeau, Mo.)
© 2007 Wenjian Wang, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Cable-stayed bridges -- Testing
Neural networks (Computer science)
Structural analysis (Engineering) -- Mathematical models
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b6126416~S5
Wang, Wenjian, "Structural condition assessment of the Bill Emerson Memorial Cable-Stayed Bridge using neural networks" (2007). Doctoral Dissertations. 1732.