Modern architecture promotes a high demand for transparent building envelopes. Typically, glass façades are designed to meet 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. Based on this, a sufficiently large database was created and employed to train and evaluate several machine learning algorithms. Finally, the best performing algorithm was used to be integrated into a comprehensive web-based program, the SOUNDLAB AI Tool, which has recently been published. This program provides rapid analysis and an accurate prediction for any glazing assembly, interlayer type and gas filling towards different sound insulation values using Machine Learning. Recent work of Kuraray and M&M considers Machine Learning based structural performance analysis of different glass systems under loadings and will soon be released as STRENGTHLAB AI Tool. The aim of these programs is to provide designers, engineers and architects with an effective and economically efficient tool to facilitate ecologic yet reliable planning in terms of acoustic and structural properties.
SOUND LAB AI Tool - Machine learning for sound insulation value predictions
Company: Kuraray Europe GmbH, Hattersheim, Germany
About the speaker:
Ingo Stelzer is a Senior Technical Program Manager at Kuraray Europe GmbH. With more than 20 years of professional experience in the glass and facade industry he has been involved into the realization of many glass construction projects and related R&D activities worldwide. He is also an active member of DIN, EN & ISO Code Work Groups and other industry associations. In year 2000 he received his engineering degree at the University of Hanover. Positions before joining Kuraray have been at DuPont de Nemours as a Senior Consultant and the SEELE-Group as the head of the glass processing division SEDAK.