Real estate asset operations typically represent around 80% of investment and management costs, with space management and system monitoring being crucial for user well-being.The AECO industry is moving towards a data-driven model, combining various type of data from various sources. In addition, the inclusion of sensor systems in buildings is leading to a shift towards a distributed semantic approach, requiring a deeper understanding of reality and the ability to link different ontologies from various fields. So, the need to address challenges such as data interoperability, integration of different semantic domains, and improvement of information exchange processes has recognized semantic web technologies as a powerful tool for enhancing the value of BIM models by facilitating data integration and enabling the application of complex queries across multiple data sources. The proposed paper aims to analyze the state of art about IoT and semantic web technologies, and their possible integration in a BIM environment to aid decision-making in building maintenance. It will provide a first ongoing approach for the digitalization of the assets managed by the University of Florence's Building Area. The study shows the two different workflows set up to connect BIM models with sensor data. The first one involved the use of DTH22 environmental sensors integrated through Node-Red to the open source platform Snap4City for data management; the second one involved the installation of LORAWAN sensors within a building and the use of a property platform, Niagara Tridium, for data manipulation and sampling to be convolved within a BMS platform. The work is part of a larger EU Next Generation research project titled, "BIM2DT. BIM-to-Digital Twin: information management to support decision-making in the building life cycle".

Historical Building Information Modeling (H-BIM) has been widely documented in literature and is becoming more popular with government bodies, who are increasingly choosing to make its use mandatory in public procurements and contracts. Although the system seems to be one of the best approaches for managing data and driving the decision-making process, several difficulties arise due to the amount of effort required in the initial phases, when the data derived from a geometrical survey must be converted into parametric elements. Moreover, users must decide on a “level of geometrical simplification” a long time in advance, and this inevitably leads to a loss of geometrical data. From this perspective, our research describes a procedure to optimize the workflow of information for existing artefacts, in order to achieve a “lean” H-BIM. In this article, we will analyse two aspects: the first relates to the level of accuracy in a digital model created from the two different point clouds achieved from laser scanner and form images, while the second concerns the conversion of this information into parametric elements (Building Object Models- BOMs) that need to have specific characteristics. The case study we are presenting is the “Ponte Giorgini” (“Giorgini Bridge”) in Castiglione della Pescaia (Grosseto – Italy).

The implementation of geometric, alphanumerical and documentary data within BIM models is opening up interesting scenarios in the integrated management of existing infrastructure works. In particular, data from the application of sensors for structural monitoring of historic bridges can originate a flow of information exchange between real artifacts and Digital Twin capable of activating effective reactive or planned responses in the operation and maintenance phase of the asset by the facility manager. This paper intends to outline a BIM-oriented process workflow, which from the creation of parametric objects for infrastructural works using Scan-to-BIM acquisition techniques and procedures, arrives at the implementation of models aimed at the management of data from incoming and outgoing sensors towards analysis, supervision and control systems of the facilities organization. The case study of the Toppoli Bridge over the Arno River along the S.P. 64 road in the Province of Arezzo (IT), an artifact dating back to the early decades of the 1900s and built using traditional construction techniques and recently subject to renovation, is addressed. The experience conducted has highlighted how the BIM methodology for information management of existing assets, effectively responds to the current needs of monitoring and control of historical infrastructure assets, being able to integrate with multiple information management systems of the facilities organization, as well as the latest AI and AR technologies.

In recent years, Facility Management has undergone significant technological and methodological advancements, primarily driven by Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM), and Computerized Maintenance Management Systems (CMMS). These innovations have improved process efficiency and risk management. However, challenges remain in asset management, maintenance, traceability, and transparency. This study investigates the potential of blockchain technology and non-fungible tokens (NFTs) to address these challenges. By referencing international (ISO, BOMA) and European (EN) standards, the research develops an asset management process model incorporating blockchain and NFTs. The methodology includes evaluating the technical and practical aspects of this model and strategies for metadata utilization. The model ensures an immutable record of transactions and maintenance activities, reducing errors and fraud. Smart contracts automate sub-phases like progress validation and milestone-based payments, increasing operational efficiency. The study’s practical implications are significant, offering advanced solutions for transparent, efficient, and secure Facility Management. It lays the groundwork for future research, emphasizing practical implementations and real-world case studies. Additionally, integrating blockchain with emerging technologies like artificial intelligence and machine learning could further enhance Facility Management processes.