Pavement degradation: a city-scale model for San Francisco, USA
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Publication Date
2018-09Journal Title
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
ISSN
2397-8759
Volume
171
Issue
3
Pages
93-109
Language
en
Type
Article
This Version
AM
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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
Data from long-term systematic pavement condition surveys provide the opportunities to better understand 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 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 (INLA) method is used
for the Bayesian regression. It was found that: (1) the non-spatial model including only coarse categories of
pavement types is too simple to provide a good fit to the data; (2) 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 (DIC) and slightly smaller fitting and testing errors; (3) only the spatial model can reveal
the spatial clustering of streets where high/low degradation rates concentrate.
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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