UofT's Digital Twin team aims to lead a transformative shift in the applications of information systems in the facility management and built environment. The goal is to support virtualization of industry practices, including integrated modeling, process automation, and data-driven decision making:
The Center will house data about projects from participating partners—starting with data about UofT's 150 buildings. The data will span structured and unstructured data; building, operator, and user-generated data; historical, real-time, and simulated-futures data. The Center will demonstrate the use of best practices in data management, investigate policies for the collection and audit of data, and examine the applicability of open data systems, including generating security/access rights best practices.
Data analytics and machine learning
Using machine learning to advance data-driven culture in the built environment, with particular emphasis on the use of analytics to generate business intelligence tools for enhancing project commissioning, advancing sustainable practices, and exploring new horizons in human-building interactions. The agenda spans four modes:
- Deployment: use BIM-based software to establish reliable means for data collection, sharing, and access.
- Analytics: Integrating IoT data, project documents, stakeholders' input to create soft-sensing mechanisms, where algorithms can detect new facts or events. Use predictive analytics to support proactive decision making. Use prescriptive analytics to assess the potential of future work/operations scenarios.
- Experimental: act as a testbed for advanced practice in process automation and streamlined information flows—deliver the right data to the right person at the right time.
- Explorative: investigate the potential of inductive programming and cognitive computing to advance virtualization, the concept of interactive buildings, and algorithmic governance of facilities