Improving Reliability of Markov-based Bridge Deterioration Model using Artificial Neural Network

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Title Improving Reliability of Markov-based Bridge Deterioration Model using Artificial Neural Network
Author Bu, Guoping; Lee, Jaeho; Guan, Hong; Blumenstein, Michael Myer; Loo, Yew-Chaye
Publication Title IABSE-IASS 2011 Symposium - Taller, Longer, Lighter
Year Published 2011
Place of publication United Kingdom
Publisher IABSE
Abstract Forecasting long-term performance of bridge by deterioration model is a crucial component in a Bridge Management System (BMS). Markovian-based models are one of the most typical methods to predict long-term bridge performance. The Markovian-based model is selected for predicting bridge deterioration, because it is the most widely accepted prediction model and has been adopted by most State-of-the-Art BMSs. The Markovian-based model is based on transition matrix obtained from overall condition rating of bridges in a network. The change in condition ratings with time provides typical deterioration rates, which can normally be determined from a non-linear regression analysis. Reliable regression analysis requires either large bridge network or sufficient historical condition ratings to obtain accurate transition probability for bridges. Markovian-based model prediction is a simple way to forecast long term performance of individual bridge. However, most bridge agencies do not have adequate condition rating records. This has become a major shortcoming in deterioration modelling. To minimise the abovementioned problem, this paper presents modified Markovian method using previously developed BPM. The BPM is able to generate missing historical condition ratings thereby providing more historical trend of condition depreciation. In this study, BPM-generated condition ratings are used for regression analysis to obtain reliable transition probability required by the Markovian-based model. The results of the proposed study are compared with those of a typical Markovian-based model to identify the advantage of BPM and limitations for further development.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2011 IASBE. The attached file is posted here in accordance with the copyright policy of the publisher, for your personal use only. No further distribution permitted. Use hypertext link for access to conference website.
Conference name 35th International Symposium on Bridge and Structural Engineering (IASBE)
Location London, United Kingdom
Date From 2011-09-20
Date To 2011-09-23
Date Accessioned 2012-03-14
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Infrastructure Engineering and Asset Management
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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