Many transportation infrastructures all around the world are facing new challenges in terms of ageing and loss of performance. The infrastructural asset managers are required to perform scrupulous control of the health condition of the infrastructures over time and to execute the required maintenance works. In this context, digital twin models of the infrastructures should have a key role to simplify and speed up the procedures for proper maintenance. This paper discusses the advantages of developing digital twin models for the management of infrastructures, with a focus on bridges. In particular, the role of dynamic tests performed on bridges for the development of digital twin models is addressed, paying attention to test procedures and requirements. Issues such as the quality of instrumentation, the numerosity, and layout of sensors, and the acquisition and post-processing procedures are addressed through applications to two real bridge case studies. Both infrastructures are multi-span pre-stressed RC bridges that were dynamically tested after the restoration and seismic upgrading works. Results of ambient vibration tests and operational modal analyses are described, providing an idea of dynamic test requirements, as well as their use within the framework of the digital twin model creation.

This paper presents the results and interpretations of static and dynamic tests that were executed on a newly built cable-stayed steel-concrete composite bridge during the final proof testing. A brief description of the structure, the testing methodology, and the used instrumentation are presented. Then, the test results are widely discussed and interpreted in order to evaluate the bridge performance during the proof test and also to understand the usefulness of each performed test in a proof test framework. All the collected experimental data are also compared to the numerical ones that were obtained through a refined finite element model, in order to check the behavior of the structure. The outcomes of the present work can offer references for the proof testing and monitoring of cable-stayed bridges.

Europe faces many problems connected to ageing infrastructure which was built in the second half of the 20th century. Bridges are one of the crucial elements of these infrastructures. In recent years, European countries have witnessed many failures of bridges across the continent. For example, the collapse of Viadotto Polcevera in Genoa caught the attention of society regarding its tragic consequences. Therefore, engineers must deal with the assessment of existing bridges which is essential for proper decision-making. Condition rating systems for bridges vary from country to country. Consequently, these differences in the methodology can lead to different conclusions related to the future service of assessed structures. For these reasons, this paper briefly describes condition rating systems for road bridges in Italy, Slovakia, and Portugal and defines the differences in the methodology. Subsequently, the obtained conclusions are compared and discussed. The aim of the paper is to encourage standardization in the assessment of bridge health conditions within European countries, highlighting the differences in the current systems adopted by various countries.

Floods pose a critical threat to bridge infrastructure, which plays an essential role in transportation networks and economic resilience. This review examines state-of-the-art Structural Health Monitoring (SHM) technologies tailored to mitigate flood risks, focusing on their real-world applications in flood-prone bridges. A central feature of this review is the extensive use of case studies, illustrating diverse SHM methods applied globally to monitor challenges such as debris accumulation, hydrodynamic forces, and scour—primary causes of bridge failures. These examples provide detailed insights into technologies like sonar-based devices, scour probes, photographic monitoring, rotation- and vibration-based techniques. By showcasing specific case studies—such as bridges monitored using smart magnetic rocks, Interferometric Synthetic Aperture Radar (InSAR), and fibre optic sensors—the review highlights practical outcomes, demonstrating how SHM systems enhance resilience through early detection and predictive maintenance. It also explores the challenges of implementing these systems, including environmental sensitivity, cost, and data complexity, while identifying gaps in integrating hydraulic and structural data for holistic risk assessments. This review advocates for multidisciplinary collaboration and advanced data-driven solutions, such as AI-based predictive maintenance, to address climate change impacts and increasing flood risks. By bridging cutting-edge research with real-world applications, this article provides actionable insights into scalable, adaptive SHM solutions, inspiring engineers and researchers to develop more resilient infrastructure for a changing world.