Resumen

ANDRADE, Alexis; CASTILLO, Gabriela  y  CHACATER, Cristian. Effects of initial data variability on pavement condition index and prediction of pavement deterioration. Novasinergia [online]. 2021, vol.4, n.1, pp.102-114.  Epub 01-Jun-2021. ISSN 2631-2654.  https://doi.org/10.37135/ns.01.07.06.

Abstract: This study's objective was to evaluate the effects of the variability of the data collected in the field for the determination of the pavement condition index (PCI) and the prediction of its deterioration through Pearson's coefficient of variation. Several investigations compare pavement condition assessment methods and apply deterioration prediction models without considering the existing subjectivity in the field data collection and its influence on their results. A field evaluation tool was developed following the PCI methodology guidelines to collect data and evaluate the variability of results using the double-blind technique applied to 20 experts. In the processing and subsequent interpretation of data, the effects produced by the variability of data collection in the results of the pavement evaluation and in the prediction model were found. When evaluating the method's results, it was found that the factor that influences the existence of variability is the number of pavement damages (19 damages). A hierarchical analytical process (AHP) uses criteria such as the frequency with which each damage is found, the degree of affectation, and the percentage of each damage found. The weighting results based on expert criteria made it possible to reduce the number of damages to 9 more representative ones and thus reduce the effects of the variability of the data taken in the field.

Palabras clave : Analytic hierarchy process; decision making; flexible pavement deterioration; highways and roads; pavement evaluation; pavement maintenance; pavement management; transportation engineering; urban pavements.

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