Assessment of uncertainty propagation using first-order Markov chain for maintenance of pavement degradation
The first-order Markov Chain (MC) is used to predict the degradation of three types of pavements (rigid, semi-rigid, and mix) utilising database in the five departments in the West of France. The assessment of uncertainty in the MC evolution is presented through studying the trend of mean and standard deviation, for components of the transition probabilities (TP) using different time steps (2, 3, 4, 5 and 6 years). The results show that the trend of rigid pavements is constant with time in terms of coefficient of variation. For semi-rigid and mix pavements, the trend of the standard deviation was constant with time. These statistical properties offer the opportunity to provide uncertainty modelling of TP. The propagation of uncertainty for 2 and 6 years time steps through the prediction of pavement condition index is also performed for analysing the effect of the uncertainty. The authors compare the profile of states obtained from each time step in view to analyse the short (2 years) and medium term (6 years) potential of prediction.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
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Supplemental Notes:
- © 2019 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Issa, Amjad
- Schoefs, Franck
- Publication Date: 2020-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1841-1852
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Serial:
- International Journal of Pavement Engineering
- Volume: 21
- Issue Number: 14
- Publisher: Taylor & Francis
- ISSN: 1029-8436
- Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html
Subject/Index Terms
- TRT Terms: Composite pavements; Deterioration; Markov chains; Pavement performance; Rigid pavements; Uncertainty
- Geographic Terms: France
- Subject Areas: Highways; Pavements;
Filing Info
- Accession Number: 01759348
- Record Type: Publication
- Files: TRIS
- Created Date: Nov 30 2020 11:48AM