Simulations of fungal decay risk were run on two similar building geometries exposed to typical annual climate conditions of two different geographical locations, Brunswick (Germany) and Cairns (Australia). The simulations were conducted to capture the effect of wind-driven rain and solar irradiation exposure over nodes of the common building geometry. The moisture content and temperature variations were then calculated point-by-point using simulation outputs, climate data and various models. A supervised machine-learning algorithm using artificial neural networks was used to calculate moisture content to more efficiently handle processing requirements. Time series of moisture content and temperature were used as input into fungal decay models, and in turn, service life planning (SLP) frameworks, where cumulative daily dose was used as the risk metric. Here, we applied the established SLP framework used in project CLICKdesign, which uses a dose-response exposure model in combination with the Meyer-Veltrup resistance model. With this specific SLP framework, various materials can be evaluated or troubleshot based on their adherence to design life specifications. Dose represents the material climate (MC and temperature), and adding surface conditions as opposed to ambient macro climate estimates presents a step forward in capturing the microclimate surrounding the material. The examples shown indicate the importance of addressing the unique variation introduced with the combination of geometry and geographical location.

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