An outdoor-to-indoor sound propagation modelling framework for evaluating noise exposure applications

Environmental noise exposure has shown to have significant negative effects on people’s lives. In noise exposure studies, outdoor noise levels are usually preferred over indoor levels for investigating exposure-response relationships, introducing a systematic risk of bias. Hence, an outdoor-to-indoor sound propagation modelling framework is defined for estimating indoor noise levels based on outdoor levels. Particularly, by expressing outdoor and indoor sound propagation via energetic models and façade (multi-component and multi-layered) structures via computational sound insulation models (Transfer Matrix Method), outdoor-based indoor impulse responses can be generated. To validate the framework, a case study was conducted, showing that measured and simulated sound insulation were in good agreement. Finally, this framework was applied to generate datasets of outdoor and indoor noise levels (noise indicators) based on scenarios of outdoor environment, indoor environment, and façade structures. This allows the training of statistical learning approaches for estimating indoor noise levels and identifying important predictors. Results show that a random-forest approach outperforms a stepwise and a neural network approach across all the employed noise indicators (RMSE < 2 dB). These models enable the estimation of indoor sound exposure levels based on outdoor levels as well as the exploration of exposure-response relationships in locations with known built environment characteristics.