Modeling of Field Sound Insulation for Multi-Layered CLT Floor Assemblies Using Artificial Neural Networks
AbstractA means prediction tool based on an artificial neural network approach is developed to predict standardized level differences and standardized impact sound pressure levels for multi-layered CLT-based floor systems. The collected data are 104 field sound insulation measurements in one-third-octave bands from 50 Hz to 5 kHz. The acoustic measurements were implemented in 15 different buildings in Europe and for different room functions and sizes. Various structural parameters were organized to develop the network model, such as floor components, surrounding wall types and their components, junction types and their visco-elastic interlayer, receiving room volume, surface separating area, and more. The developed network demonstrates satisfactory results in predicting standardized field airborne and impact sound insulation curves across all frequencies. The weighted standardized level differences DnTw are estimated with 1 dB variation, while up to 2 dB for standardized impact sound pressure level L0 nTw. A good correlation is highlighted for airborne estimations in the middle frequencies (200 - 1000 Hz), while higher frequencies often reveal some deviations. However, impact insulation estimations showed better accuracy in the high-frequency range (1.25 - 5 kHz).
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