Pavement degradation: a city-scale model for San Francisco, USA
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Publication Date
2018-09-01Journal Title
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
ISSN
2397-8759
Publisher
Thomas Telford Ltd.
Volume
171
Issue
3
Pages
93-109
Language
en
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Zhao, B., Silva, E., & Soga, K. (2018). Pavement degradation: a city-scale model for San Francisco, USA. Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 171 (3), 93-109. https://doi.org/10.1680/jsmic.18.00001
Abstract
<jats:p>Data from long-term systematic pavement condition surveys provide opportunities to understand better the pavement degradation process. To provide more accurate predictions on future pavement conditions, spatial conditions are incorporated into degradation models of pavements in this paper. Long-term, city-scale pavement condition data from the San Francisco open data portal in USA are used to test and guide model development. Spatial and non-spatial degradation models are developed and compared with parameter estimations carried out using the Bayesian approach. Specifically, the integrated nested Laplace approximation method is used for the Bayesian regression. It was found that (a) the non-spatial model including only coarse categories of pavement types is too simple to provide a good fit to the data; (b) for models with fine categories (individual street segments), the spatial model is more preferable than the non-spatial model due to its lower deviance information criterion and slightly smaller fitting and testing errors; and (c) only the spatial model can reveal the spatial clustering of streets where high/low degradation rates concentrate.</jats:p>
Sponsorship
Cambridge Trust, the Alan Turing Institute
Identifiers
External DOI: https://doi.org/10.1680/jsmic.18.00001
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288194
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http://www.rioxx.net/licenses/all-rights-reserved
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