Identifying error and maintenance intervention of pavement roughness time series with minimum message length inference

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Title: Identifying error and maintenance intervention of pavement roughness time series with minimum message length inference
Author(s): Byrne, Matthew; Albrecht, David; Sanjayan, Jay
Abstract: Pavement roughness is a useful measure of pavement condition. One method of comparing alternative sections of pavements is the roughness progression rate (RPR). The objective of this paper is to describe difficulties in identifying RPR from real data and provide a new type of criteria to overcome these difficulties. Selecting appropriate regression functions for time-series roughness presents two major problems. Roughness time series can include roughness data that appear erroneous, acting independent of the observed time-series trend. Including likely error values will bias the calculated RPR. 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 minimum message length (MML) criterion to select RPR is introduced and is referred to herein as MML RPR. We perform simulated comparisons of common segmentation criterion and conclude that MML RPR is the preferred criterion.
Publication type: Journal article
Source: International Journal of Pavement Engineering, Vol. 11, no. 1 (Feb 2010), pp. 37-47
Publication year: 2010
FOR Code(s): 090503 Construction Materials; 090506 Structural Engineering
Keyword(s): Deterioration; Minimum message length; Pavements; Progression rates; Roughness; Time series
Publisher: Taylor & Francis
ISSN: 1029-8436
Publisher URL: http://dx.doi.org/10.1080/10298430802621549
Peer reviewed