SOUNDLAB AI Tool-Machine learning for sound insulation value predictions

Modern architecture promotes a high demand for transparent building envelopes. Typically, glass façades are designed under a variety of objectives, one of which is to meet sound insulation requirements. Reliable and fairly accurate estimation of the sound insulation properties of different glass assemblies becomes time-consuming and difficult due to the complexity of experimental testing or numerical simulations. Therefore, this paper presents a Machine Learning approach for predicting the acoustic properties (weighted sound insulation value RW, STC, OITC) of different glazing systems. For this purpose, extensive research was conducted on various glazing systems consisting of different glass assemblies with varying glass, cavity and interlayer thicknesses as well as different types of interlayer and gas fillings.