PSI - Issue 84

Enrico Pasquale Zitiello et al. / Procedia Structural Integrity 84 (2026) 360–367

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nature, integrating data from sensors, IoT systems, and real-time analytics, with the aim of monitoring the infrastructure's performance during operation. While BIM is primarily oriented towards the design and construction phases, the Digital Twin focuses on operational and maintenance management, offering an evolving representation of the facility based on real, up-to-date data. From this perspective, as stated by Zitiello et al. (2025)(7), BIM constitutes the information basis of the digital twin, while the Digital Twin represents its natural evolution towards an intelligent infrastructure management system, particularly relevant in the case of bridges, where the integration between structural models, monitoring data and maintenance information allows the transition from reactive strategies to predictive maintenance policies(8). The bibliometric analysis conducted using VOSviewer, based on the co occurrence of keywords in the relevant scientific literature, allows for a systematic delineation of the main research directions related to digital twins and intelligent infrastructure management. The map highlights the relational structure of the scientific domain, where the size of the nodes is proportional to the frequency of the terms and the distance between them reflects the degree of thematic correlation in the analyzed contributions. The process reveals distinct yet highly interconnected clusters. A first, particularly central, thematic core is represented by predictive maintenance, closely associated with machine learning, artificial intelligence, and data analytics techniques, reflecting the growing trend toward data-driven approaches for managing the life cycle of infrastructure. A second cluster focuses on structural health monitoring and real-time monitoring, highlighting the importance of sensor and data acquisition technologies for the continuous assessment of structural health. Another thematic area concerns building information modeling, connected to management systems, energy efficiency, and information modeling, confirming the role of BIM as a structured basis for data integration. The central position of the term "digital twin," located at the intersection of information modeling, structural monitoring, and predictive maintenance, highlights how research is converging toward integrated models capable of combining digital representation, real-time acquisition, and advanced decision-support tools(9). Overall, the bibliometric map paints a picture of a progressively maturing scientific field, characterized by a growing integration of information modeling, advanced sensors, and predictive algorithms, with increasingly relevant applications in the bridge infrastructure sector. 3. Materials and methods

Fig. 2. Methodological workflow based on the procedure

The proposed methodology is based on the integration of digital twins, information modelling and IoT monitoring systems, taking the form of a closed-loop iterative process that continuously connects the physical infrastructure to its virtual model(11). The primary objective is to create a dynamic system capable of acquiring data in real time, interpreting it through engineering models and supporting maintenance decisions throughout the entire life cycle of the bridge. The operational flow is divided into a sequence of interconnected phases, beginning with infrastructure modelling and ending with verification of the effectiveness of the interventions, generating a cyclical process of