This paper describes a new proactive approach for facility managers to assess and manage complaints of “sick buildings”.
The assessment of the “sick building” syndrome problem is multifaceted and should include both objective data such as those collected and analyzed by industrial hygienists, and subjective data such as occupant perception measurements, usually gathered by surveys. The data for this research, both objective and subjective, were provided on a wide variety of office buildings and were analyzed using an artificial neural‐network based model.
Current literature on the subject suggests that the cause of poor indoor environments involves many variables interacting in an unlimited number of combinations. Using a blended definition of a narrowly defined “sick building”, a framework for a decision‐making support system for facility managers is provided.
The data collection was limited to Southeastern US commercial office buildings, but the model has global applicability.
Recommendations are presented to help facility managers better understand the complex nature of the indoor environment based on this research.
This research and data analysis can be tailored to and is applicable to any building type.
