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Monitoring the actual conditions of critical infrastructure assets is a priority for administrators to guarantee high-standard in terms of structural stability, and operational safety and to prevent damages and deterioration. Nowadays, most protocols for assessing road pavement conditions, including viaducts and bridges, are based on visual inspections conducted by specialized operators and, rarely, the local application of ground-based technologies such as IoT Wireless Sensors, laser scanners, Falling Weight Deflectometers, and accelerometers. However, the high costs of maintenance operations and on-site surveys, which involves the partial or total temporary closure of the infrastructure for the duration of the tests, still limit the implementation of these procedures at the network-scale level. Accordingly, the definition of innovative, automatic, and low-cost procedures for continuous monitoring operations, especially for road pavements and concrete bridges, is still an open challenge. In this context, recent developments in Artificial Neural Networks (ANNs) have opened new possibilities in the automatic recognition and geo-localization of damages affecting critical transportation assets, such as bridges and viaducts. This research presents an experimental application for the automatic detection of deteriorated bridge-deck expansion joints and the classification of pavement distresses based on Artificial Neural Networks models. More specifically, the methodology is founded on the latest generation of ANNs algorithms, among which “YOLO v5”. For this purpose, an experimental evaluation is conducted by the acquisition of a large dataset of imageries of road damages and bridge-deck joints, collected by open-source datasets, and the processing by ANNs. Furthermore, a field experiment was conducted in order to collect several videos of bridges, selected as case-studies, by means of an instrumented vehicle, through a High-Definition resolution camera. The acquired images were post-processed and implemented for the training phases of the ANNs and the verification phases of the developed model. The application of the presented approach provides a reliable, affordable, and promising methodology for the automatic identification and localization of pavement distresses and bridge-deck joints damages, to be rapidly managed and decisively actioned by administrative authorities and asset owners, giving crucial information that could be implemented for the prioritisation of maintenance activities within Asset Management Systems. The findings of this study demonstrate that ANNs approaches and Deep Learning algorithms, can be applied to complement Non-Destructive Remote Sensing technologies (e.g., satellite radar interferometry, Laser Scanners), localizing automatically the pavement and infrastructure damages, paving the way for integrated approaches in the smart monitoring of infrastructure assets.
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