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Study On Condition Assessment, Prediction Of Track Beam Bridge And The Maintenance Methods

Posted on:2011-06-07Degree:MasterType:Thesis
Country:ChinaCandidate:C LiuFull Text:PDF
GTID:2132360305460512Subject:Structural engineering
Abstract/Summary:PDF Full Text Request
Urban rail transit holds the key positions in urban transportation system, the performance of them are related to economic development and people's lives. As the first monorail in China, the research on condition assessment,prediction and maintenance methods is very important to insure to make decision of the track beam bridge maintenance scientifically, reasonably and extend the bridges'service life, guarantee the use of the bridge.Assessment of the track beam bridge is the preparatory work of the bridges' condition prediction and maintenance decision. This paper analyzes and instantiate the application of Uncertain Analytical Hierarchy Process in the compositive evaluation of the track beam bridge. In order to reflect the status of the bridge structure roundly, this paper discussed and instantiate the variable weight synthesizing which can reflect the equilibrium of every estates. After discussing the basic theory of the GM(1,1) model and Markov Chain model, this paper adopted the Markov Chain model and the Grey-Markov chain prediction model to predict the track beam bridge's technology condition. A sample proves the calculating result from the Grey-Markov chain prediction model is much closer to the reality. By means of consulting experts, typical bridge maintenance countermeasure repository and decision-making tree are built to choose the most rational maintenance measure.With the combination of scientific management and bridge maintenance management from the view of evaluation and decision-making and with bridge maintenance management procedure as the clue the paper provides a reference for the research and exploration of the maintenance management system.
Keywords/Search Tags:track beam bridge, grey prediction, Markov chain, maintenance
PDF Full Text Request
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