Advanced Digital Twins in Bridge Management: An Approach for Structural Monitoring

Faced with today's critical issues in the management and maintenance of infrastructures - due to factors such as age, structural complexity and the limitations of traditional inspections - this study systematically addresses the entire set of maintenance issues, proposing the integrated use of innovative digital technologies as an answer. The research presents an advanced methodology based on the synergy between Building Information Modeling (BIM), Internet of Things (IoT) and Machine Learning (ML), with the aim of supporting the predictive and dynamic management of infrastructures through the creation of Digital Twins. In this paper, this methodology is applied to two case studies of bridges in Italy, which are currently being monitored. The application focuses on the initial stages of the process: digital modelling of the works (with the creation of BIM models containing static data), installation of sensors for the collection of dynamic data in real time, and subsequent integration of this information for the generation of a functional Digital Twin. This unified model allows not only the management of warning systems based on predefined thresholds, but also the elaboration of in-depth structural diagnoses to support the planning of maintenance interventions. The proposed methodology also represents a strategic step towards the evolution of advanced predictive models, favouring a more proactive, efficient and sustainable approach to maintenance.