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1.	Bader Eddin, Mohamad, et al. (författare) A sound insulation prediction model for floor structures in wooden buildings using neural networks approach 2021 Ingår i: Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. - 0736-2935. - 9781732598652 Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract Reliable prediction tools are yet to be developed for estimating the accurate acoustic performance of lightweight structures, which are vital especially in the design process. This paper presents a sound insulation prediction model based on artificial Neural Networks (NN) to estimate acoustic performance for airborne and impact sound insulation of floor structures. At an initial stage, the prediction model was developed and tested for a small amount of data, specifically 67 laboratory measurement curves in one third octave bands. The results indicate that the model can predict the weighted airborne reduction index Rw for various floors with a maximum error of 1 dB. The accuracy decreases with errors up to 9 dB for the weighted index for impact sound Ln,w, in cases of complex floor configurations due to large error deviations in high frequency bands between the real and estimated values. The model also shows a very good accuracy in predicting the airborne and impact sound insulation curves in the low frequencies, which are of higher interest usually in building acoustics.

Bader Eddin, Mohamad, et al. (författare)
A sound insulation prediction model for floor structures in wooden buildings using neural networks approach
2021
Ingår i: Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. - 0736-2935. - 9781732598652
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Reliable prediction tools are yet to be developed for estimating the accurate acoustic performance of lightweight structures, which are vital especially in the design process. This paper presents a sound insulation prediction model based on artificial Neural Networks (NN) to estimate acoustic performance for airborne and impact sound insulation of floor structures. At an initial stage, the prediction model was developed and tested for a small amount of data, specifically 67 laboratory measurement curves in one third octave bands. The results indicate that the model can predict the weighted airborne reduction index Rw for various floors with a maximum error of 1 dB. The accuracy decreases with errors up to 9 dB for the weighted index for impact sound Ln,w, in cases of complex floor configurations due to large error deviations in high frequency bands between the real and estimated values. The model also shows a very good accuracy in predicting the airborne and impact sound insulation curves in the low frequencies, which are of higher interest usually in building acoustics.

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