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Monthly Attenuation Prediction for Asphalt Pavement Performance by Using GM (1, 1) Model

Auteur(s):

Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-11
DOI: 10.1155/2019/9274653
Abstrait:

Due to the uncertainty and variability of various factors affecting the pavement performance, the change in pavement performance cannot be completely determined. In addition, this uncertainty is not accurately reflected by the pavement performance prediction model. In particular, the gray GM (1, 1) model is very suitable due to it is ability to better predict the existing situation of a domestic asphalt pavement along with the actual performance of a road surface of the “small sample, poor information” gray system. In this regard, the gray GM (1, 1) model is being increasingly used to forecast the performance of an asphalt pavement. When a gray GM (1, 1) model is used to predict the performance of an asphalt pavement, the condition number of the GM (1, 1) model matrix is too large, which, in turn, leads to the deviation of calculation and even wrong results in some cases. This study analyzed the reason for a large condition number of the GM (1, 1) model matrix. Combined with the numerical characteristics of the pavement condition index (PCI) and pavement quality index (PQI), this study focused on the annual, monthly, and daily attenuations of PCI and PQI to the condition number of the GM (1, 1) model matrix. Accordingly, we propose a method to forecast the performance of an asphalt pavement using the monthly attenuation of PCI and PQI. The PCI and PQI in Hunan Province in recent years have been predicted, and the findings reveal that the prediction GM (1, 1) model for the monthly attenuation of PCI and PQI not only effectively lowered the condition number of the matrix but also ensured that the relative error was small.

Copyright: © 2019 Limin Tang et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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  • Reference-ID
    10314308
  • Publié(e) le:
    07.06.2019
  • Modifié(e) le:
    11.07.2019