Andrzej Poýarycki - Pavement diagnosis accuracy with controlled application of artificial neural network (Vol X, No 4)

Vilnius Gediminas Technical UniversityRiga Technical UniversityTallinn University of TechnologyBaltic Road Association


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2015 Impact Factor: 0.519 ©2015 Thomson Reuters, 2015 Journal Citation Report®

ISSN 1822-427X print
ISSN 1822-4288 online



"The Baltic Journal of Road and Bridge Engineering"
Vilnius: Technika, 2015, Vol X, No 4, p. 355-364

Andrzej Poýarycki

Pavement diagnosis accuracy with controlled application of artificial neural network

DOI: 10.3846/bjrbe.2015.45
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.
Keywords: fatigue life assessment of pavement, perturbations of data set in ANN methods, PMS databases, thickness identification by ANN.

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