| Περίληψη: | The operational phase of a real estate asset accounts for approximately 80% of the overall investment and management costs throughout the entire life cycle of the building, and the activities of space management and monitoring of building components and systems play a crucial role in ensuring the well-being and health of users. The AECO (Architecture, Engineering, Construction, and Operation) industry is transitioning towards a new framework governed by data-driven processes. In this context, Building Information Modeling (BIM) can support the utilization of big data generated throughout different stages of the building's life cycle, thereby establishing itself as a dynamic repository of information at the center of a constellation of systems used by a Facility Management body to achieve specific objectives (such as CAFM, ERP, BMS, etc.). The proposed study aims to define a processing framework for the collection and management of data aimed at the implementation of DT of existing real estate assets, created based on the integration between BIM platforms and IoT technology oriented to subsequent developments of big data analytics and AI applications. The objective is to support in the operational phase of buildings the decisions of the various operators involved in planning scheduled and/or corrective maintenance actions and to generate content, recommendations, best practices by formulating predictive analysis on managed assets. In particular, a critical analysis is made of the various approaches available for the definition of an IT architecture to support IoT reference models, which will find application in the monitoring of some existing assets of the University of Florence's real estate managed by the Building Area, digitally implemented on a BIM platform. The contribution is part of a broader research activity carried out as part of the PNR Project, "BIM2DT. BIM-to-Digital Twin: information management to support decision-making in the building life cycle."
|
|---|
