Identifying error and maintenance intervention of pavement roughness time series with minimum message length inference
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.
Document Type: Research Article
Affiliations: 1: Asset Management, ARRB Research, Melbourne, Vic., Australia 2: Clayton School of Information Technology, Monash University, Melbourne, Vic., Australia 3: Department of Civil Engineering, Monash University, Melbourne, Vic., Australia
Publication date: 2010-02-01