Monte Carlo Simulation to Evaluate Mould Growth in Walls: The Effect of Insulation, Orientation, and Finishing Coating

Abstract

Mould growth can have severe consequences both on the health of occupants and on constructions' durability. Mould growth is a very complex process that depends on many factors such as temperature and relative humidity, presence of nutrients, and exposure time. Several mould prediction models, which allow estimating mould growth in building components and performing risk analysis, are available in the literature, such as the updated VTT model or the Biohygrothermal model. A Portuguese typical wall configuration was used for a sensitivity analysis. The importance of insulation (with and without insulation), orientation (north and south), and finishing coating (gypsum-based rendering, medium density fibreboard (mdf), and untreated wood) for the mould growth phenomenon was tested using both the updated VTT model and the Biohygrothermal model. A total of 12 case studies were investigated. The influence of indoor climate was evaluated by simulating 200 scenarios previously generated using the Monte Carlo method. Each of the scenarios has been applied to the 12 case studies, and 2400 hygrothermal simulations were carried out. Initially, the case studies were simulated using WUFI 1D since both mould growth models require the superficial temperature and relative humidity as input. Simulations were carried out for a one-year period. The updated VTT model produced results (mould index-M) ranging between 0.4 (gypsum-based rendering, insulated, and south oriented wall) and 5.9 (untreated wood, noninsulated, and north oriented wall) and the Biohygrothermal model (mould growth) between 10.1 and 406.4 mm for the same case studies. Despite that the effect of the orientation of the wall could be identified, the importance of insulation and nature of substrate was more evident. Although the two models produced overall comparable results, some differences could be found, creating the opportunity to discuss their strengths and weaknesses as well as their sensitivity to the input parameters.