A Spatial-Bayesian Technique for Imputing Pavement Network Repair Data
This article describes how pavement construction and repair history is necessary for several pavement management functions such as developing pavement condition prediction models and developing maintenance and rehabilitation (M&R) trigger values that are based on past repair frequencies. The article describes how it is often difficult to integrate M&R data with condition data since these data are often stored in disparate heterogeneous databases. The article provides a computational technique for estimating construction and M&R history of a pavement network from the spatiotemporal patterns of its condition data. The technique is founded on Bayesian and spatial statistics and searches pavement condition data in groups of adjacent pavement sections for evidence of repair. The technique developed in this article was applied to a pavement network in Texas and has been found to have a 74% precision and a 95% accuracy in estimating repair history data.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
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Authors:
- Saliminejad, Siamak
- Gharaibeh, Nasir G
- Publication Date: 2012-9
Language
- English
Media Info
- Media Type: Print
- Features: Figures; Maps; References; Tables;
- Pagination: pp 594-607
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Serial:
- Computer-Aided Civil and Infrastructure Engineering
- Volume: 27
- Issue Number: 8
- Publisher: Blackwell Publishing
- ISSN: 1093-9687
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667
Subject/Index Terms
- TRT Terms: Condition surveys; Databases; Pavement maintenance; Pavement management systems; Paving; Rehabilitation
- Geographic Terms: Texas
- Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements; I60: Maintenance;
Filing Info
- Accession Number: 01444810
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 31 2012 9:02AM