In the constantly growing need for sustainable mobility and transportation, on-site inspections of existing reinforced concrete (RC) bridges are critical in ensuring the safety of such infrastructures. However, surveying RC bridges presents several challenges, such as the high costs and effort required by the surveyors, the subjectivity in assessing identified defects, and the possible lapses of attention when inspections are systematically repeated on different bridges. Hence, traditional methods of on-site inspection can be enhanced by leveraging digital innovations and by developing new instruments that support road management companies in ensuring the safety of the existing infrastructure. Among the new technologies, deep learning-based object detection systems provide promising and effective solutions. As such, this research proposes a new, simple, intuitive and efficient tool to support engineers and surveyors in assessing the health state of existing RC bridges. To this end, domain experts gathered and labelled a dataset of real images containing typical defects found in existing RC bridges. Consequently, an improved version of YOLO11, embedding attention mechanisms to allow the network to focus on the most relevant details in each image, was trained, tested, and validated on the provided dataset, showing an overall improvement of quantitative metrics such as precision and recall, while retaining enough computational efficiency to allow real-time implementation on constrained devices. Visual explanations achieved via the Eigen-CAM algorithm were also exploited to evaluate the reliability of the predictions. The model was finally embedded in an end-to-end tool offering a graphical user interface (GUI) to allow an effective interaction between the domain expert and the machine. Overall, the proposal revealed its potential to improve the effectiveness of the survey, lowering the burden on surveyors and engineers and providing a reliable method to improve the overall security in large RC bridges portfolios.

This paper presents an early warning system to investigate deformations in simply supported concrete girder bridges over time, using the information content provided by satellite data, integrated with other available sources. The safety of the existing bridges is a priority for transportation management companies, which should carry out continuous and accurate monitoring campaigns, by exploiting traditional time‐ and cost‐consuming activities that cannot be widely applied to a bridge portfolio scale. To reduce management costs and to define reliable prioritization schemes, new cost‐effective technologies can be involved such as the satellite‐based Multi‐Temporal Interferometry Synthetic Aperture Radar (MTInSAR). This technique can represent a valuable option for observing the displacements induced by different actions and to relate the identified behaviour to possible or future fails. This paper presents an early warning system aimed at exploring possible anomalies in simply supported reinforced concrete girder bridges by efficiently elaborating MTInSAR, combined with additional data (e.g., environmental temperature and structural information knowledge). The proposed framework allows manipulating the persistent scatterers’ information to derive longitudinal and vertical displacements over time. These are compared to appropriate thresholds leading to potential early warnings aimed at supporting road managers in undertaking future surveillance actions. The proposed procedure was tested on a case study, defined according to the most spread typology of bridges in Italy. This application highlights the advantages of the proposed framework which allows for a cost‐effective long‐term monitoring with outputs that can be automatically updated over time and suitable for network‐scale early warning detection.