Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Abstract

The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.

Federated Data Modeling for Built Environment Digital Twins

“…This is the reason why the IFC data model is federated with the BrickSchema (Balaji et al 2016), instead of extending it. According to the approach described in Xie et al (2021b), the IFC and the Brick data models are leveraged to access both the spatial and system hierarchies, characterising the spaces of the Alan Reece building (Figure 7, Brick section adn Figure 8b).…”

“…However, seamless data access and exchange capabilities need to be granted to unlock the potential of data-driven methods capable of triggering these new outcomes. An example is the need to access and consume real-time sensor readings; geometry data of the built assets; system hierarchies; properties of the assets for fine grain energy monitoring: a service that enables better understanding and control of the energy usage in buildings (Xie et al 2021b). However, the required data is generally handled according to organisation-specific or proprietary formats, models, and requirements that hinder the data interoperability and reuse in multiple applications.…”