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Soft Computing Models to Predict Pavement Roughness: A Comparative Study

Auteur(s):


Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2018
Page(s): 1-8
DOI: 10.1155/2018/5939806
Abstrait:

Pavement roughness as a critical determinant of public satisfaction can potentially play a major role in road or highway resource allocation to competing pavement resurfacing projects. With this in mind, the aim of the present paper is to develop an accurate model for the prediction of pavement roughness in terms of the International Roughness Index (IRI) using artificial neural networks (ANNs) and support vector machines (SVMs). The modeling is based on pavement roughness data collected periodically for a high-volume motorway during a seven-year period, on a yearly basis. The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. Further, the analysis results produce evidence in support of the statement that both models are capable to predict accurately pavement roughness; hence, they are deemed useful for supporting decision making of pavement maintenance and rehabilitation strategies.

Copyright: © 2018 Panos Georgiou 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.

  • Informations
    sur cette fiche
  • Reference-ID
    10176573
  • Publié(e) le:
    30.11.2018
  • Modifié(e) le:
    02.06.2021
 
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