Occupant density impact on building maintenance: Data-driven approach for university buildings

Abstract

Building maintenance needs in public buildings depend on occupant activities and presence. Research should understand how different types of occupant density patterns can be used to forecast the likelihood of specific kinds of maintenance requests. This research adopts a data-driven approach to evaluate experimental-based correlations between maintenance work orders number (relating to a set of Italian university buildings as a relevant case study) and occupant density, thanks to exceptional conditions due to COVID-19 pandemic, which significantly altered building use. Results offer a power-law-based correlation model, confirming that the reduction of occupant density in the COVID-19 lock-down phases impacted the number and perceived severity, but not the typologies, of maintenance work orders. The retrieved correlation model occupant could be directly used to define and prioritize maintenance strategies given occupant density. Future research could use the model to define outsourcing and contract definitions starting from historical data on maintenance actions.