Modeling field measurements of sound insulation for multi-layered CLT-based floor systems: A means of a prediction model using artificial neural networks

Abstract

A neural network-based prediction tool is developed to calculate the standardized sound level differences and the standardized impact sound pressure levels for multi-layered CLT-floor systems. The data for this model is derived from 104 sound insulation measurements in one-third-octave bands from 50 Hz to 5 kHz taken from 15 buildings in Europe with different room sizes and functions. The network model is developed using various structural parameters such as floor components, wall types, junction types and interlayers, receiving room volume, surface separating area, and more. The network developed shows good performance in predicting standardized airborne and impact sound insulation curves over all frequencies. The weighted standardized level differences DnTw are estimated with an accuracy of 1 dB, while the standard impact sound pressure level LnTw′ is accurate up to 2 dB. The airborne predictions are correlated in the middle-frequency range (200–1000 Hz), while some deviations may occur in higher frequencies. Impact insulation estimations, on the other hand, are more accurate in the high-frequency range (1.25–5 kHz). A sensitivity study is conducted to understand the model’s dependence on parameters. In both types of estimations, the direct sound path through the floor is the most influential factor, with the flanking paths affecting the results in the second order. Additionally, the volume of the receiving room significantly affects impact estimations at low frequencies. The study’s results also emphasize the importance of a visco-elastic interlayer for accurate airborne predictions in all frequency ranges.