Assessing Mold Risks in Buildings under Uncertainty

• Main Author: Moon, Hyeun Jun
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: Mold; Uncertainty analysis; IAQ; Performance indicator; Building simulation; Uncertainty (Information theory); Indoor air pollution Computer simulation; Molds (Fungi) Control Computer simulation; Monte Carlo method
• Online Access: http://hdl.handle.net/1853/7279

Microbial growth is a major cause of Indoor Air Quality (IAQ) problems. The implications of mold growth range from unacceptable musty smells and defacement of interior finishes, to structural damage and adverse health effects, not to mention lengthy litigation processes. Mold is likely to occur when a favorable combination of humidity, temperature, and substrate nutrient are maintained long enough. As many modern buildings use products that increase the likelihood of molds (e.g., paper and wood based products), reported cases have increased in recent years. Despite decades of intensive research efforts to prevent mold, modern buildings continue to suffer from mold infestation. The main reason is that current prescriptive regulations focus on the control of relative humidity only. However, recent research has shown that mold occurrences are influenced by a multitude of parameters with complex physical interactions. The set of relevant building parameters includes physical properties of building components, aspects of building usage, certain materials, occupant behavior, cleaning regime, HVAC system components and their operation, and other. Mold occurs mostly as the unexpected result of an unforeseen combination of the uncertain building parameters. Current deterministic mold assessment studies fail to give conclusive results. These simulations are based on idealizations of the building and its use, and therefore unable to capture the effect of the random, situational, and sometimes idiosyncratic nature of building use and operation. The presented research takes a radically different approach, based on the assessment of the uncertainties of all parameters and their propagation through a mixed set of simulations using a Monte Carlo technique. This approach generates a mold risk distribution that reveals the probability of mold occurrence in selected trouble spots in a building. The approach has been tested on three building cases located in Miami and Atlanta. In all cases the new approach was able to show the circumstances under which the mold risk could increase substantially, leading to a set of clear specifications for remediation and, in for new designs, to A/E procurement methods that will significantly reduce any mold risk.