Moving from building information models to digital twins for operation and maintenance

Abstract

With the rising adoption of building information modelling (BIM) for asset management within the architecture, engineering and construction sectors, BIM-enabled asset management during the operation and maintenance phase has been increasingly attracting more and more attention in both research and practice. This paper provides a comprehensive review and analysis of the development of state-of-the-art research and industry standards that impact on BIM and asset management within the operation and maintenance phase. However, in the aspects of both information richness and analytical capability, BIM is not always enough in delivering effective and efficient asset management, particularly in the operation and maintenance phase. Therefore, a framework for future development of smart asset management is proposed, integrating the concept of digital twins. Digital twins integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple information sources. The findings will contribute to inspiring novel research ideas and promote widespread adoption of digital-twin-enabled asset management within the operation and maintenance phase.

Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment

“…The integration of POEs in a BIM approach enables the connection between POE data and the digital model [8,12,43], with the advantages of defining a single source and storage of POE and building data, integrating structured data into the BIM, and identifying POE data and related issues in a visual representation of the building space [8,44,45]. Despite the advantages of adopting BIM during the operational phase, a BIM approach for asset management lacks of information richness, analysis, and simulation capability, which are usually manually implemented and time-consuming when using a BIM model [20]. In addition, an effective and efficient management of buildings during the operational phase strongly rely on continuous flows of real time data regarding the building, its performances, and conditions [20,46].…”

“…Despite the advantages of adopting BIM during the operational phase, a BIM approach for asset management lacks of information richness, analysis, and simulation capability, which are usually manually implemented and time-consuming when using a BIM model [20]. In addition, an effective and efficient management of buildings during the operational phase strongly rely on continuous flows of real time data regarding the building, its performances, and conditions [20,46]. However, BIM models present limitations for the integration with different data sources and systems, e.g., sensor data, and lack of automatic updating and evolution over time [20].…”

“…In addition, an effective and efficient management of buildings during the operational phase strongly rely on continuous flows of real time data regarding the building, its performances, and conditions [20,46]. However, BIM models present limitations for the integration with different data sources and systems, e.g., sensor data, and lack of automatic updating and evolution over time [20]. Therefore, in order to overcome these limitations, the definition of a Digital Twin is investigated.…”

“…In fact, a fundamental characteristic of a DT is the connection, alignment, and reciprocity between the physical and virtual part [19]. Therefore, a key aspect is the data collection process, ensuring data quality on which the correct digital representation of the physical phenomenon depends [20,21], since, in order to obtain satisfactory results, is essential to ensure the quality of input data [22]. In this perspective, fundamental steps are the selection of sensor types that are most suitable for the specific application [23], the spatial distribution of sensors in the indoor spaces [24], and the setting and calibration of the IoT sensor system [25,26], to allow a correct detection and collection of data.…”

Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration

“…Besides, sensor data can accumulate extremely fast. Data transformation strategies and storage work of such big data volume also need improvements [9]. (3) Typical in-use management software like BIM platforms mainly focus on browsing or checking business data with a 3D model, but not mass analysis.…”