Prediction of Sound Insulation Using Artificial Neural Networks—Part I: Lightweight Wooden Floor Structures

Abstract

The artificial neural networks approach is applied to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed based on 252 standardized laboratory measurement curves in one-third octave bands (50–5000 Hz). Physical and geometric characteristics of each floor structure (materials, thickness, density, dimensions, mass and more) are utilized as network parameters. The predictive capability is satisfactory, and the model can estimate airborne sound better than impact sound cases especially in the middle-frequency range (250–1000 Hz), while higher frequency bands often show high errors. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum error of 2 dB. However, the error increased up to 5 dB in the worse case prediction of the weighted normalized impact sound pressure level Ln,w. The model showed high variations near the fundamental and critical frequency areas which affect the accuracy. A feature attribution analysis explored the essential parameters on estimation of sound insulation. The thickness of the insulation materials, the density of cross-laminated timber slab and the concrete floating floors and the total density of floor structures seem to affect predictions the most. A comparison between wet and dry floor solution systems indicated the importance of the upper part of floors to estimate airborne and impact sound in low frequencies.

Prediction of Sound Insulation Using Artificial Neural Networks—Part II: Lightweight Wooden Façade Structures

“…This reveals that the main difficulties in estimation of sound reduction values are appeared near those bands. Similar problems with ANN models were also reported in other studies [25,45]. Table 3 represents root-mean-square errors (RMSE) in the prediction of airborne sound reduction curves.…”

“…This action leads to hysteretic energy losses, which contributes to sound attenuation [17]. The latter agrees with [45] and contrasts with another study [67] that showed the unimportance of V in the low frequency range. Contrary to the thickness of exterior studs in a façade wall, the thickness of interior studs is a factor of higher weight on prediction across all frequencies.…”

“…The total thickness of a façade structure influences the predictions at all frequencies, which is also concluded in [67]. Additionally, the total density affects the prediction the most across all frequencies, as also reported in [45]. The attribution spectrum of the total density of a façade reveals a peak near 150 Hz and a dip near 1.25 kHz, which is probably due to effects of fundamental and critical frequencies, respectively.…”

“…A complementary study was conducted [45] using 252 standardized laboratory measurements performed on lightweight wooden floors with construction parameters, such as material type, material thickness and density, area of the floor structure, etc. When more data are included, a better accuracy in predicting L n,w is achieved, within 5 dB variations.…”

WoodRise 2022

“…An accurate forecasting of sound insulation performance of double structures has been and still a challenge (Vigran, 2014). The applications of machine learning have been widely used to solve complex problems in various fi elds, such as: image classifi cation, speech recognition (Abdel-Hamid, 2013), but few studies in building acoustics (Bader Eddin, 2022). The scope of this study is to develop a prediction tool based on artifi cial neural networks for airborne sound insulation estimation of façade structures.…”