Predictive modeling and estimation of moisture damages in Swedish buildings: A machine learning approach

•Coupling damage data and building registers enables damage mapping and prediction.•The links between damage cause, driving part, and damaged component are clarified.•Tree ensemble binary relevance classifiers predict over 90 % of damage accurately.•Building type implicates the likelihood of deformation and odor to certain extents.•Predicted damage rates are lower than statistics for existing building stock. Identifying potential moisture damage is crucial for maintenance practices and assurance of well-being of occupants. However, due to limited information availability and standardization, assessing damage prevalence on the building stock scale remains understudied. By combining investigation records and building databases, this study leverages data analytic techniques and machine learning modeling to characterize damage pathology and predict its occurrence in Swedish buildings. The interrelationships between damage-specific attributes and their associations with building parameters of several damage types were analyzed using feature selection, forming the basis for developing predictive models. Results show that multilabel classifiers outperform binary classifiers for every damage type, with lead tree ensemble models achieving minimum average AUCPR and F2 of 0.85 for microbial growth, 0.87 for deformation, 0.91 for odor, and 0.95 for water leakage. The identified patterns were interpreted and verified against descriptive statistics. The binary relevance models estimate that one-third of school buildings, 20 % of commercial and office buildings, and 15 % of residential dwellings in regional building stock contain moisture damage. These findings advance the quantification of moisture damage by providing new knowledge and approaches for appraising moisture damage likelihood at aggregated and individual building levels, thereby aiding in moisture safety evaluations and preventive maintenance efforts.