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The Pavement diagnosis accuracy with controlled application of artificial neural network

Author(s):
Medium: journal article
Language(s): English
Published in: The Baltic Journal of Road and Bridge Engineering, , n. 4, v. 10
Page(s): 355-364
DOI: 10.3846/bjrbe.2015.45
Abstract:

Results of research studies, the amount of input data available in pavement management system databases, and artificial intelligence methods serve as versatile tools, well-suited for the analysis conducted as a part of pavement management system. The key source of new and to be employed knowledge is provided. In terms of e.g. assessing thickness of bituminous pavement layers, the default solution is pavement drilling, but for the purposes of pavement management it is prohibitively expensive. This paper attempts to test the original concept of employing an empirical relationship in an algorithm verifying results produced by the artificial neural network method. The assumed multistage asphalt pavement layer thickness identification control process boils down to evaluating test results of the road section built using both, reinforced and non-reinforced pavement structure. By default, the artificial neural network training set has not included the reinforced pavement sections. Hence, it has been possible to identify “perturbations” in assumptions underlying the training set. Pavement test section points' results are indicated in the automated manner, which, in line with implemented methods, is not generated by perturbations caused by divergence between actual pavement structure and assumptions taken for purposes of building pavement management system database, and the artificial neural network learning dataset is based on.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/bjrbe.2015.45.
  • About this
    data sheet
  • Reference-ID
    10084641
  • Published on:
    09/10/2018
  • Last updated on:
    09/10/2018
 
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