With the advent of smart environments the requirements for diagnostics and prognostics techniques gained a lot of interest. Lately, the Industry 4.0 paradigm is starting to expand also in the smart road, referred to in this context as "maintenance 4.0". In such a context, the possibility to predict the conditions of a road network allows to deliver a preventive maintenance that can strongly reduce the costs and avoid severe consequences. However, considering the actual pavement management state of the art, it is evident the huge amount of heterogeneous data necessary to perform this challenging tasks. Leveraging the Cloud and Edge technologies, this paper proposes a web Geographical Information System (GIS) platform capable to dynamically collect and analyze several type of sensor data for the management of road pavements. On top of that, the platform is able to compute advanced indexes and metrics that are used to produce a maintenance proposal scheme. Experimental results present a preliminary case study on a real motorway and demonstrate the effectiveness of the proposed platform as a support tool during the maintenance process.
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