I-Corps: Translation Potential of Digital Twin and Artificial Intelligence for Building Maintenance Management

The broader impact of this I-Corps project is the development of a scalable and adaptable technology utilizing digital twins and artificial intelligence (AI) to build maintenance management. The built environment accounts for approximately 45% of global carbon emissions and comprises almost 75% of building operating costs. Cutting-edge technologies such as digital twins and artificial intelligence (AI) are promising for synthesizing big data from sensors, automation systems, maintenance activities, and building occupancy.

The proprietary technology of this I-Corps project will transform building operations and maintenance practices into a data-driven structure to predict possible failures and defects of critical building systems with simulations for risk scenarios. Together with the operational benefits, this technology will enhance occupant comfort, safety, and well-being, as well as improve the longevity, durability, and sustainability of the built environment. Overall, the technology will lead to buildings that are smarter and more responsive.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a technology for building maintenance management that utilizes a digital twin that connects various data resources such as building automation, controls, and sensors to maintenance management systems with artificial intelligence (AI) to predict possible failures and defects with simulations for risk scenarios.

The technology enables the interoperability of various data sets and provides simulations of potential failures with risk scenarios such as cost, safety, and impact on critical systems. Predictive models improve efficiency by utilizing big data to uncover trends and anomalies that are obscured in manual observation methods. The combination of digital twin technology and AI opens a wide range of possibilities to visualize building information based on operational data.

Action plans support the decision-making processes with effective resource allocation and reduced equipment downtime.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.