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.
Highlights
In order to comply with level two and level three standards of analysis accuracy as per NCHRP Project 1-37A:2004 Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures, the major proportion of computations, related to pavements, starts with the preparation of large inputs datasets (Zeghal, El Hussein 2008)
artificial neural network (ANN) which is in the case of pavement management system (PMS) databases, is a natural choice because the enormous input data resources are available for the training sets
When it is decided to use ANN for purposes of pavement layer identification, it should be taken into consideration that these methods are problematic due to the uncertainty caused by potential “perturbations”, which occur in assumptions underlying the training set
Summary
In order to comply with level two and level three standards of analysis accuracy as per NCHRP Project 1-37A:2004 Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures, the major proportion of computations, related to pavements, starts with the preparation of large inputs datasets (Zeghal, El Hussein 2008). Highway engineering requires advances in computer science (Koch et al 2015), sources for datasets is a critical starting point for modelling road pavements using the theory of artificial neural networks (Kim et al 2009) or another theories which make possible to analyse the incomplete data (Luo 2011). The possibility of using artificial intelligence (AI) in analysing risk profile of building pavement structures (Ceylan, Gopalakrishnan 2011) facilitates building hybrid models, which take into account both, stochastic processes concerning timevarying pavement parameters and uncertainty, related to poor build quality of pavement structure. There are many other authors (Bilodeau, Doré 2014; Pasquini et al 2013) who discuss the methods of using different range of information (compared to many PMS standard procedures), concerning falling weight deflectometer (FWD), based on bearing capacity of pavement analysis
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