Identifying error and maintenance intervention of pavement roughness for sealed granular time series with MML piecewise inference

M. Byrne, D. Albrecht, J. Sanjayan

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Abstract

A useful measure of performance comparison between sections of pavement is the roughness progression rate (RPR) which describes increase in roughness per time increment. This rate can be used to predict future levels of pavement roughness and a comparison of deterioration rates for alternate pavements within a network. Selecting appropriate regression functions to describe the RPR for each pavement interval presents two major problems. Roughness time-series can include roughness data that appears error, appearing to act independent of the observed time series trend. Including likely error values will bias the calculated roughness progression rate. The problem of identifying likely error is made more difficult with the possibility of maintenance intervention which may reduce the roughness level and/or progression rate. A criterion to select RPR based upon Minimum Message Length (MML) inference is introduced in this paper and is referred to herein as MML RPR. We propose a method to learn RPR which is the combination of two parts. A segmentation model is used to learn whether any maintenance has caused a change in roughness progression. Secondly, a classified mixture model is used to identify likely error. We perform simulated comparisons comparing common segmentation criterion using unclassified mixture models (ML, AIC, AlCc and BIC) and conclude that MML RPR is the preferred criterion.

Original languageEnglish
Title of host publicationCanadian Society for Civil Engineering - Annual Conference of the Canadian Society for Civil Engineering 2008 - "Partnership for Innovation"
Pages2577-2586
Number of pages10
Volume4
Publication statusPublished - 1 Dec 2008
EventAnnual Conference of the Canadian Society for Civil Engineering 2008 - "Partnership for Innovation" - Quebec City, QC, Canada
Duration: 10 Jun 200813 Jun 2008

Conference

ConferenceAnnual Conference of the Canadian Society for Civil Engineering 2008 - "Partnership for Innovation"
CountryCanada
CityQuebec City, QC
Period10/06/0813/06/08

Cite this

Byrne, M., Albrecht, D., & Sanjayan, J. (2008). Identifying error and maintenance intervention of pavement roughness for sealed granular time series with MML piecewise inference. In Canadian Society for Civil Engineering - Annual Conference of the Canadian Society for Civil Engineering 2008 - "Partnership for Innovation" (Vol. 4, pp. 2577-2586)