Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe effective movement of goods services. Management, therefore, plays a key role ensure consistent efficient operation. However, significant resources required perform necessary maintenance activities achieve high levels service. Pavement can typically be very expensive decisions needed concerning planning prioritizing interventions. Data towards enabling adequate but in many instances, there limited available information especially small or under-resourced urban road authorities. This study develops roadmap help these authorities by using flexible data analysis deep learning computational systems highlight important factors networks, which used construct models that predict future intervention timelines. A case Palermo, Italy was successfully developed demonstrate how the techniques could applied appropriate feature selection prediction based on sources. The workflow provides pathway more pavement management practices readily adapted different environments. takes another step automating system.