Abstract
An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.
Highlights
Blasiis, Chiara Ferrante, Road networks and airport areas are key assets [1] for both developed and developing countries [2]
Assuming as current state-of-practice (CSP) the artificial neural networks (ANNs) model implemented in Toolbox, the comparison between this simplified model and the one proposed by the auMATLAB® Toolbox, the comparison between this simplified model and the one proposed thors has been considered
The performance of the model is expressed in terms of Pearson coefficient R, mean squared error MSE, and adjusted coefficient of determination R2adj
Summary
Chiara Ferrante, Road networks and airport areas are key assets [1] for both developed and developing countries [2]. Such transport infrastructures have a great impact on energy consumption for the industrial productions involved and the related emissions, given that their service life fixed is in several tens of years [4] In this regard, the prediction of long-term performance is fundamental, the mechanical strength (stiffness modulus) from repeated investigations over time, in order to properly implement maintenance and rehabilitation (M&R) strategies to achieve sustainable technical, economic, and environmental solutions [5]. Backcalculation is known as the parameter identification problem, and is basically an optimization process performed to obtain inverse mapping of a known constitutive relationship using discrete or continuous data points [28] It consists in a numerical analysis of the measured deflections in order to estimate the pavement layer moduli. By providing a numerical estimation of the modulus in an arbitrary location on the runway, the proposed pavement performance prediction model could allow us to identify the areas that most require maintenance interventions reducing the costs of instrumental monitoring and allowing active and efficient management [41]
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