The analysis of variability of pavement indicators: MPD, SMTD and IRI. A case study of Portugal roads

Macrotexture is one of the main pavement quality parameters considered in Portuguese Quality Control Plans which is not specifically approached in data variability analysis studies. In this study, the analysis of variance (ANOVA) – Full factorial method was used to analyse the variability of the mean profile depth, the sensor measured texture depth and also the international roughness index. The ANOVA is appropriated to assess some sources of variation such as repeatability and reproducibility, and it allows us to investigate the contribution of several factors and interaction between them on the variability of a performance parameter. In this study, data from five profilometers running on six long road sections were used. The investigated parameters were type of surface, operator and system and run repetition. Repeatability results were different for each parameter and the analysis of factors points to the necessity to further investigate the effect of the type of surface on data variability. The development of an operational plan to assure reproducibility is required.

[1]  Kiyoshi Kobayashi,et al.  A statistical deterioration forecasting method using hidden Markov model for infrastructure management , 2012 .

[2]  David W. Smith,et al.  Interrater Reliability of Manual Pavement Distress Evaluations , 2010 .

[3]  Samer Madanat,et al.  Optimal infrastructure management decisions under uncertainty , 1993 .

[4]  P. Bievre.  The 2007 International Vocabulary of Metrology (VIM), JCGM 200:2008 [ISO/IEC Guide 99]: Meeting the need for intercontinentally understood concepts and their associated intercontinentally agreed terms. , 2009 .

[5]  Elisabete F. Freitas,et al.  3D surface profile equipment for the characterization of the pavement texture - TexScan , 2010 .

[6]  Bjarne Schmidt.  EVEN Project: Experiment to Compare and Harmonize Methods for Assessment of Longitudinal and Transverse Evenness of Pavement , 2001 .

[7]  T D Gillespie,et al.  GUIDELINES FOR CONDUCTING AND CALIBRATING ROAD ROUGHNESS MEASUREMENTS , 1986 .

[8]  Jason W. Osborne,et al.  The power of outliers (and why researchers should ALWAYS check for them) , 2004 .

[9]  Mats Wiklund,et al.  Acceptance testing for road surface monitoring vehicles in Finland , 2008 .

[10]  Kevin K McGhee,et al.  Quality Management of Pavement Condition Data Collection , 2009 .

[11]  Pietro Leandri,et al.  The reliability of tests and data processing procedures for pavement macrotexture evaluation , 2011 .

[12]  Jorge A Prozzi,et al.  Roughness Model Accounting for Heterogeneity Based on In-Service Pavement Performance Data , 2010 .

[13]  Mrcrmsr W. Savnns.  I On the Calculation of International Roughness Index from Longitudinal Road Profile , 2022 .

[14]  Jean-Marc Martin,et al.  Study of Precision of Transverse Evenness Measurements in FILTER Experiment: Forum of European National Highway Research Laboratories Investigation on Longitudinal and Transverse Evenness of Roads Experiment , 2001 .

[15]  D. W. Zimmerman,et al.  Increasing the Power of Nonparametric Tests by Detecting and Downweighting Outliers , 1995 .

[16]  Steven M. Karamihas,et al.  Development of cross correlation for objective comparison of profiles , 2004 .

[17]  Luís Picado-Santos,et al.  Selection of pavement performance models for use in the Portuguese PMS , 2011 .

[18]  H Viner,et al.  Surface texture measurement on local roads , 2006 .

[19]  Afrooz Moatari Kazerouni.  Design and Analysis of Gauge R&R Studies: Making Decisions Based on ANOVA Method , 2009 .

[20]  Eun Sug Park,et al.  A Bayesian approach for improved pavement performance prediction , 2008 .

[21]  Frannie Humplick,et al.  HIGHWAY PAVEMENT DISTRESS EVALUATION: MODELING MEASUREMENT ERROR , 1992 .

[22]  Nam Lethanh,et al.  Use of exponential hidden Markov models for modelling pavement deterioration , 2013 .

[23]  Tomi Seppälä,et al.  Comparison of Modeling and Measurement Accuracy of Road Condition Data , 2006 .