A sound insulation prediction model for floor structures in wooden buildings using neural networks approach

Bard, Delphine (author): Lund University,Lunds universitet,Teknisk akustik,Institutionen för byggvetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,Engineering Acoustics,Department of Construction Sciences,Departments at LTH,Faculty of Engineering, LTH

Kouyoumji, Jean Luc (author)

Vardaxis, Nikolas Georgios (author): Lund University,Lunds universitet,Teknisk akustik,Institutionen för byggvetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,Engineering Acoustics,Department of Construction Sciences,Departments at LTH,Faculty of Engineering, LTH

Dare, Tyler (editor)

Bolton, Stuart (editor)

Davies, Patricia (editor)

Xue, Yutong (editor)

Ebbitt, Gordon (editor)

(creator_code:org_t)

2021
2021
English.
In: Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. - 0736-2935. - 9781732598652

Related links:: http://dx.doi.org/10...; show more...; https://lup.lub.lu.s...; https://doi.org/10.3...; show less...

Abstract Subject headings

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.

By the author/editor: Bader Eddin, Moh ...; Menard, Sylvain; Bard, Delphine; Kouyoumji, Jean ...; Vardaxis, Nikola ...; Dare, Tyler; show more...; Bolton, Stuart; Davies, Patricia; Xue, Yutong; Ebbitt, Gordon; show less...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Fluid Mechanics ...

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