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Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance

Auteur(s):


Médium: article de revue
Langue(s): en 
Publié dans: The Baltic Journal of Road and Bridge Engineering, , n. 1, v. 14
Page(s): 58-79
DOI: 10.7250/bjrbe.2019-14.433
Abstrait:

Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road agencies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), “homogeneity” of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.

Copyright: © 2019 Gaetano Bosurgi, Orazio Pellegrino, Giuseppe Sollazzo
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
    10311395
  • Publié(e) le:
    04.04.2019
  • Modifié(e) le:
    11.07.2019