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Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Auteur(s):



Médium: article de revue
Langue(s): anglais
Publié dans: Frontiers in Built Environment, , v. 8
DOI: 10.3389/fbuil.2022.895210
Abstrait:

The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R² value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R²) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.

Copyright: © 2022 Shadi Hanandeh, Ahmad Hanandeh, Mohammad Alhiary, Mohammad Al Twaiqat
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.

  • Informations
    sur cette fiche
  • Reference-ID
    10702911
  • Publié(e) le:
    11.12.2022
  • Modifié(e) le:
    15.02.2023
 
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