BRIDGE SERVICE LIFE PREDICTION MODEL USING THE MARKOV CHAIN
This paper describes the application of Markov chain technique in estimating bridge service life. The change of bridge conditions is a stochastic process and, therefore, the service life of bridges is related to the probabilities of condition transitions. A bridge service life prediction model, using the Markov chain, was developed to reflect the stochastic nature of bridge condition and service life. The paper includes a discussion on the concept of Markov chain, the development and application of the service life prediction model using the Markov chain, and the comparison of service life predictions by statistical and Markov chain approaches.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/0309048206
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Supplemental Notes:
- This paper appears in Transportation Research Record No. 1223, Bridge Design and Performance and Composite Materials. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
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Authors:
- Jiang, Yi
- Sinha, Kumares C
- Publication Date: 1989
Media Info
- Features: Figures; References; Tables;
- Pagination: p. 24-30
- Monograph Title: Bridge design and performance and composite materials
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Serial:
- Transportation Research Record
- Issue Number: 1223
- Publisher: Transportation Research Board
- ISSN: 0361-1981
Subject/Index Terms
- TRT Terms: Bridges; Estimating; Forecasting; Markov chains; Mathematical models; Service life; Stochastic processes
- Subject Areas: Bridges and other structures; Data and Information Technology; Design; Highways; I24: Design of Bridges and Retaining Walls;
Filing Info
- Accession Number: 00494740
- Record Type: Publication
- ISBN: 0309048206
- Files: TRIS, TRB
- Created Date: Jun 30 1990 12:00AM