Estimating airtightness is challenging due to variability in influencing factors. Existing models face limitations such as a lack of standardization, sensitivity to construction quality, insufficient representative data, and complexity that reduce their practical utility for designers. This work explores the application of a predictive approach from measured data using Generalized Linear Models (GLIM) that is applicable across diverse construction scenarios. The model incorporates 13 main variables and 4 interaction terms, which explain almost 50% of the variability in airtightness (n50). A typical case study has been chosen in order to show the model’s application, making it user-friendly. This example explores the criteria followed for each variable and building characteristics so that the model can be easily extrapolated to other contexts and applied to other cases.
