Digital Twins (DT) are powerful tools to support asset managers in the operation and maintenance of cognitive buildings. Building Information Models (BIM) are critical for Asset Management (AM), especially when used in conjunction with Internet of Things (IoT) and other asset data collected throughout a building’s lifecycle. However, information contained within BIM models is usually outdated, inaccurate, and incomplete as a result of unclear geometric and semantic data modelling procedures during the building life cycle. The aim of this paper is to develop an openBIM methodology to support dynamic AM applications with limited as-built information availability. The workflow is based on the use of the IfcSharedFacilitiesElements schema for processing the geometric and semantic information of both existing and newly created Industry Foundation Classes (IFC) objects, supporting real-time data integration. The methodology is validated using the West Cambridge DT Research Facility data, demonstrating good potential in supporting an asset anomaly detection application. The proposed workflow increases the automation of the digital AM processes, thanks to the adoption of BIM-IoT integration tools and methods within the context of the development of a building DT.

Improving the efficiency of operations is a major challenge in facility management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in silos that are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails or bureaucratic processes. Digital twins represent an emerging approach to returning awareness and control to facility managers by automating all levels of information access (from granular data to defined key performance indicators and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in facility management, including construction, operation and maintenance data, and Internet of things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece Building (Cambridge, UK) demonstrates the approach by enabling fault detection and diagnosis of the heating, ventilation and air-conditioning system for facility management.