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Molinero-Perez, N; Shane, JS; García-Segura, T; Madson, KM; Montalbán-Domingo, L; Mo, Y; Sanz-Benlloch, A; Poleacovschi, C; Pellicer, E;

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Pavement maintenance management requires artificial intelligent techniques to evaluate pavement condition automatically, predict pavement deterioration, and optimize maintenance actions under restrictive budgets. This paper presents three techniques that address the three objectives raised. First, Convolutional Neural Networks (CNN) are used to analyze images obtained by a camera installed on a vehicle. Several CNNs are trained to detect, classify, and quantify the 3D distresses. Second, pavement deterioration is predicted by Feed-forward Neural Networks (FNN). Pavement Condition Index (PCI) throughout a planning horizon is estimated from the information obtained by the inspection and the traffic and climate conditions. Third, heuristic optimization algorithms are used to determine the optimal maintenance plan. This plan indicates which sections should be repaired each year of the planning horizon and the treatment that must be used to optimize the maintenance cost, the CO2 emissions, the user cost, the network condition, and the accidents. These techniques are presented and discussed in this paper.

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