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