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The increasing demand for energy-efficient and sustainable construction necessitates the need for building envelope designs that effectively balance moisture safety with sustainability. Improper moisture management in building envelopes can lead to severe consequences, including construction damage, decreased energy performance, and health risks associated with microbial growth. For instance, water infiltration in multi-family wood-frame buildings between 1993 and 2000 caused an estimated one billion Canadian dollars in damage. Similarly, in Belgium, water-tightness issues accounted for nearly half of all documented building damage cases between 2010 and 2015, with water penetration being the most prevalent issue. In Sweden, over 30% of single-family homes have been affected by mould and other moisture-related problems, with microbial growth impacting 60–80% of single-family houses with cold attics in the Gothenburg region.

This thesis begins with a comprehensive state-of-the-art literature review of probabilistic hygrothermal analyses for building envelopes, through which critical research gaps are identified. In response to these gaps, the thesis introduces a set of probabilistic methodologies that integrate machine learning algorithms and decision analysis frameworks to optimise the design of moisture-safe and sustainable building envelopes. Three novel methodologies are proposed: Mould Reliability Analysis (MRA), Mould Sensitivity Analysis (MSA), and Inutility Decision Analysis (IDA). MRA leverages a probabilistic approach to address uncertainties in hygrothermal performance, employing a machine learning metamodel (based on random forests algorithm) to predict mould indices efficiently. MSA combines linear and non-linear sensitivity analyses to identify important variables affecting moisture-related damage. IDA expands traditional decision-making frameworks by incorporating sustainability metrics, such as life cycle costing (LCC) and life cycle assessment (LCA), alongside hygrothermal performance.

The results from case studies demonstrate the effectiveness of these methodologies. Probabilistic analyses exposed the limitations of deterministic approaches, which often underestimate moisture-related risks. The integration of machine learning significantly reduced computational time, enabling the evaluation of millions of scenarios with high precision. Sensitivity analyses pinpointed influential variables and highlighted variations in their importance under different conditions. IDA findings revealed that designs with no probability of mould growth might not always be optimal if their initial environmental and economic impacts are high. In certain cases, designs with manageable mould growth risks, lower initial costs, and environmental impacts were found to be more advantageous over the building’s lifespan.

This research demonstrates the potential of integrating AI-based probabilistic moisture safety design with sustainability to develop robustness and environmentally responsible building envelopes. The proposed methodologies provide practitioners with advanced framework to address uncertainties, enhance design robustness, and incorporate multi-criteria decision-making into construction projects. Future research opportunities include expanding the framework to encompass energy performance and health consequences, further enhancing its utility and impact in the construction industry.