Neural networks for the selection of repair methods for steel bridge
Constraints of money, manpower or
equipment make it impossible for public agencies to deal efficiently with
all bridges requiring improvement concurrently. Steel bridge assessment and
selection of the appropriate repair and retrofit methods require extended
in-situ inspections, testing and analysis, which depend on the expertise of
engineers and on empirical knowledge collected over several years.
The present research
investigates the applicability of the artificial neural networks, in order
to classify steel bridges damages and select the appropriate repair method
of the structure. At first, a database of cases histories of railroad bridge
damages and relevant repair methods is collected. The information is
translated to input variables and preprocessed. Characteristic attributes
of the dataset include damage location, condition and cause, while the
output variable is the corresponding retrofit method.
A wide range of neural
networks architectures is considered, namely SOFM, LVQ2, ART, HAVNET and
multiplayer networks. The success or the difficulty with which they
classify the input variables is investigated. This research can find practical
applications in the field of bridge management, as an attractive
alternative in the decision-making process, cost estimation and assessment
of unusual bridge damage conditions.
Last updated: 23 September, 2001 07:11:48 PM
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