Title: Using neural networks to position live loads on bridge piers
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00100723/00001
 Material Information
Title: Using neural networks to position live loads on bridge piers
Physical Description: xv, 189 p. ; ill.
Language: English
Creator: Williams, Mark Erik, 1974- ( Dissertant )
Hoit, Marc ( Thesis advisor )
Gurley, Kurtis ( Thesis advisor )
Consolazio, Gary ( Reviewer )
Fagundo, Fernando ( Reviewer )
Rabens, Michael ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2000
Copyright Date: 2000
Subjects / Keywords: Civil Engineering thesis, Ph. D
Dissertations, Academic -- Civil Engineering -- UF
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
Abstract: The structural analysis of bridges and their supporting pier foundations is an intriguing subject that is frequently debated among many structural engineers. Unlike most building designs, modern highway bridge designs must consider the variability of loads on the bridge and the uncertainty of their application. In particular, the application of vehicular live loads is not straightforward. At any given time, vehicles can traverse the bridge at unknown speeds and paths producing different force effects. Fortunately, the correct application of vehicle loads to the bridge superstructure has been documented by the American Association of State, Highway, and Transportation Officials (AASHTO) as well as by other research institutions. However, the subsequent application of these live loads to the supporting bridge piers is still not well understood and is only briefly addressed by the AASHTO-LRFD Design Specifications. A common situation arises when determining the maximum force effects on the superstructure and pier foundation. The application of vehicular live loads to the superstructure to achieve the maximum force effects in the superstructure does not necessarily produce the maximum force effects in the pier foundation. In other words, an entirely different live load application may produce the maximum force effects in the pier foundation. An exhaustive study of live load position combinations across the bridge deck can produce thousands of possible design load cases. The most critical live load positions can then be determined by studying the results of all the live load combinations. In an attempt to circumvent this tedious process, eight neural networks have been trained to predict the live load positions across the bridge width to achieve the maximum force effects in the pier foundation. The networks use geometric input parameters that describe the structure and produce truck and lane load positions in each design lane for output. Results are very encouraging though further training and network enhancements are still possible.
Subject: bridge, pier, live load
Thesis: Thesis (Ph. D.)--University of Florida, 2000.
Bibliography: Includes bibliographical references (p. 182-186).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
Statement of Responsibility: by Mark Erik Williams.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains xv, 187 p.; also contains graphics.
General Note: Vita.
 Record Information
Bibliographic ID: UF00100723
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 45296635
alephbibnum - 002566155
notis - AMT2436


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