Today, information exchange in the AECO industry at different stages of the construction process is typically done through file transfers in heterogeneous formats, with limited communication between parties. This leads to potential data management issues such as redundancy and write errors. While efforts to standardize data exchange date back to the 1990s with formats such as STEP and IFC, the challenge of interoperability remains. That is why it is important to establish integrated management systems and interoperable cloud-based technologies. The rise of open semantic standards by W3C and other organizations in recent decades has been significant, but a cohesive link between different ontologies is needed to realize Digital Twin technology, which represents the lifecycle of a building, not just the design phase. An introduction to the concepts and standards of Semantic Web technologies and their possible applications to the construction sector in the different phases of an asset’s life cycle is proposed. In particular, the paper addresses the management of existing assets by public administrations, focusing on the creation of an infrastructure that links existing traditional databases, BIM models, and dynamic data collected by IoT devices. This will be done using standards such as RDF, OWL, and SPARQL. The research is part of an ongoing NRP project called “BIM2DT. BIM-to-Digital Twin: Information Management to Support Decision-making in the Building Life Cycle”.
BIM and Data Integration: A Workflow for the Implementation of Digital Twins
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.