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Distress and profile data analysis for condition assessment in pavement management systems

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Abstract

Pavement data collection is the most expensive and time consuming component of Pavement Management System (PMS). Thus, possible methods of minimizing the need of such data might be critical in reducing pavement condition monitoring costs. Also the ability to relate pavement performance prediction models (frequently roughness based) to hot mix asphalt field performance models (distress based) provides valuable conclusions and input in pavement design, performance assessment, maintenance and rehabilitation strategies. Objective of this study was to examine whether specific distresses can influence roadway profile so as to be able to relate the two. The influence of pavement distresses on road profile has been investigated over the years. However, past studies provided conflicting conclusions. Thus, in this study an alternative approach was considered due to the availability of high quality and detailed distress data collected with the Laser Crack Measurement System (LCMS) of the Automatic Road Analyzer, ARAN. As it was expected, specific distresses have higher impact in longitudinal roughness since they are present on the roadway surface at regular intervals (i.e., specific frequencies). For this reason, instead of using summary indexes (i.e., International Roughness Index (IRI), Pavement Condition Index (PCI)), the Power Spectral Density (PSD) of the roadway profile at specific frequency bandwidths was considered along with distresses. The analysis indicated that a specific subset of distresses is affecting roughness at definite wavelength frequencies. Alligator cracking and rutting standard deviation provided the best correlation. IRI was correlated better with distress (e.g. rutting standard deviation) at lower profile frequencies. At high frequency domain (i.e., below 0.8 m wavelengths) better correlation between IRI and high severity cracking was observed through the PSD. Considering multiple frequencies in the regression models between roughness and distresses, the goodness of fit has not necessarily improved. However, the role of different bandwidths was evident. In addition to the specific results, the methodology presented in this study can be used elsewhere to assess potential relations between pavement roughness and distress components.

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Acknowledgements

This work has been partially financed by the University of Catania within the project “Piano della Ricerca Dipartimentale 2016–2018” of the Department of Civil Engineering and Architecture.

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Authors and Affiliations

  1. Department of Civil Engineering and Architecture, University of Catania, Via Santa Sofia 64, I-95125, Catania, Italy

    S. Cafiso & A. Di Graziano

  2. Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland, 20742, USA

    D. G. Goulias

  3. Department of Technology and Society, Lund University of Technology, Lund 118, 221 00, Sweden

    C. D’Agostino

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  1. S. Cafiso
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  2. A. Di Graziano
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  3. D. G. Goulias
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  4. C. D’Agostino
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Correspondence to S. Cafiso.

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Peer review under responsibility of Chinese Society of Pavement Engineering.

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Cafiso, S., Di Graziano, A., Goulias, D.G. et al. Distress and profile data analysis for condition assessment in pavement management systems. Int. J. Pavement Res. Technol. 12, 527–536 (2019). https://doi.org/10.1007/s42947-019-0063-7

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  • DOI: https://doi.org/10.1007/s42947-019-0063-7

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