This paper presents a methodological and operational workflow for the implementation of Digital Twins (DT) in the construction sector through the integration of Building Information Modeling (BIM) and Internet of Things (IoT) systems. The research addresses the growing need for structured data management across the lifecycle of built assets, emphasizing the transition from static BIM models to dynamic, data-driven environments capable of supporting real-time monitoring and decision-making.
The proposed framework combines federated BIM models (in IFC format) with real-time sensor data collected from IoT devices, enabling the creation of a unified information system where geometric, semantic, and environmental data converge (Fig. 1, p. 826). The workflow is structured into six phases—creation, communication, aggregation, analysis, insight, and action—defining a progressive integration between physical assets and digital environments. Data collected from sensors (e.g. temperature and humidity) are processed through edge computing systems and integrated into the Snap4City platform, where they are visualized via dashboards and linked to BIM components (Figs. 6–7, pp. 831–832).
The results demonstrate that the integration of BIM and IoT enables the development of digital twins that support facility management, predictive maintenance, and performance monitoring. While artificial intelligence is identified as a future extension for data analytics and predictive evaluation, the current contribution focuses primarily on data integration, interoperability, and visualization. The study highlights both the potential and the limitations of current DT implementations, particularly regarding semantic interoperability and data standardization.