Pavement Diagnosis Accuracy with Controlled Application of Artificial Neural Network

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 multi-stage 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.

[1]  Fevzullah Temurtas,et al.  An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease , 2012, Comput. Electr. Eng..

[2]  BilodeauJean-Pascal,et al.  Estimation of tensile strains at the bottom of asphalt concrete layers under wheel loading using deflection basins from falling weight deflectometer tests , 2012 .

[3]  Z. Luo,et al.  Pavement performance modelling with an auto-regression approach , 2013 .

[4]  Morched Zeghal,et al.  Assessment of analytical tools used to estimate the stiffness of asphalt concrete , 2008 .

[5]  Xiaoning Zhang,et al.  Development of asphalt pavement fatigue cracking prediction model based on loading mode transfer function , 2012 .

[6]  Adelino Ferreira,et al.  Road network pavement maintenance optimisation using the HDM-4 pavement performance prediction models , 2012 .

[7]  Hui Gao,et al.  Road maintenance optimization through a discrete-time semi-Markov decision process , 2012, Reliab. Eng. Syst. Saf..

[8]  Edmundas Kazimieras Zavadskas,et al.  Multi-criteria Risk Assessment of a Construction Project , 2013, ITQM.

[9]  Halil Ceylan,et al.  Computationally efficient surrogate response models for mechanistic–empirical pavement analysis and design , 2011 .

[10]  Günter Gauglitz,et al.  Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements , 2003 .

[11]  Mehmet Saltan,et al.  Backcalculation of pavement layer moduli and Poisson's ratio using data mining , 2011, Expert Syst. Appl..

[12]  Halil Ceylan,et al.  Airfield pavement deterioration assessment using stress-dependent neural network models , 2009 .

[13]  Keith Worden,et al.  Uncertainty analysis of a neural network used for fatigue lifetime prediction , 2008 .

[14]  BilodeauJean-Pascal,et al.  Direct estimation of vertical strain at the top of the subgrade soil from interpretation of falling weight deflectometer deflection basins , 2014 .

[15]  Sunghwan Kim,et al.  Neural Networks Application in Pavement Infrastructure Materials , 2009, Intelligent and Soft Computing in Infrastructure Systems Engineering.

[16]  H I Park,et al.  Prediction of Resilient Modulus of Granular Subgrade Soils and Subbase Materials using Artificial Neural Network , 2009 .

[17]  Paul W. Fieguth,et al.  A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.

[18]  Jie Zhang,et al.  Selective combination of multiple neural networks for improving model prediction in nonlinear systems modelling through forward selection and backward elimination , 2009, Neurocomputing.

[19]  Gilda Ferrotti,et al.  Laboratory characterisation and field validation of geogrid-reinforced asphalt pavements , 2013 .