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dc.contributor.authorZhao, Bingyuen
dc.contributor.authorSilva, Elisabeteen
dc.contributor.authorSoga, Kenichien
dc.date.accessioned2019-01-18T00:31:53Z
dc.date.available2019-01-18T00:31:53Z
dc.date.issued2018-09en
dc.identifier.issn2397-8759
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288194
dc.description.abstractData 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.
dc.description.sponsorshipCambridge Trust, the Alan Turing Institute
dc.languageenen
dc.titlePavement degradation: a city-scale model for San Francisco, USAen
dc.typeArticle
prism.endingPage109
prism.issueIdentifier3en
prism.publicationDate2018en
prism.publicationNameProceedings of the Institution of Civil Engineers - Smart Infrastructure and Constructionen
prism.startingPage93
prism.volume171en
dc.identifier.doi10.17863/CAM.35510
dcterms.dateAccepted2018-10-29en
rioxxterms.versionofrecord10.1680/jsmic.18.00001en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-09en
dc.contributor.orcidZhao, Bingyu [0000-0002-2369-7731]
dc.contributor.orcidSilva, Elisabete [0000-0002-5816-6447]
dc.contributor.orcidSoga, Kenichi [0000-0001-5418-7892]
dc.identifier.eissn2397-8759
rioxxterms.typeJournal Article/Reviewen
rioxxterms.freetoread.startdate2020-01-11


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