Leveraging Bim for Automated Fault Detection in Operational Buildings

Building Information Modeling (BIM) is an increasingly popular method for generating and managing facility information during the life cycle of a building, ranging from facility conceptualization, through design, construction and its operational life. Organizations involved in Facility Management (FM) have the opportunity to use BIM as a knowledge repository to document evolving facility information and to support decisions made by the facility managers during the operational life of a facility. This paper demonstrates the potential of using BIM to develop algorithms that automate decision making for FM applications. The potential of utilizing BIM as an analysis tool is demonstrated through the scenario of HVAC (Heating, Ventilation, and Air Conditioning) system failure in an operating facility. In case of a typical HVAC malfunction today, facility occupants record complaints in a ticketing database maintained by the FM organization. Upon receiving notification of HVAC system failure, facility inspectors visit the location to confirm the reported failure. Upon confirmation, facility managers review building plans and specifications to develop a detailed plan of action to repair any HVAC components suspected of damage. Based on the plan of action, inspectors visit the facility to inspect and, in case of damage, repair the appropriate HVAC system components. These FM practices – as currently implemented across the industry – are labor intensive, time consuming, and often rely on unreliable and outdated information. To address these shortcomings, the authors propose an alternative methodology of HVAC fault detection in operational buildings. The authors implement an algorithm that leverages complaint ticket data and automates BIM to determine potentially damaged HVAC system components. Based on the list of HVAC components suspected of damage, the algorithm develops a plan of action for the facility inspectors. Finally, the authors discuss the advantages of the proposed method as well as the challenges of implementing automated BIM-enabled decision making processes in the FM industry.