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

Mohamad Bader Eddin; Sylvain Menard; Delphine Bard; Jean Luc Kouyoumji; Nikolas Georgios Vardaxis

doi:10.3397/IN-2021-2619

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

Mohamad Bader Eddin, Sylvain Menard, Delphine Bard, Jean Luc Kouyoumji, Nikolas Georgios Vardaxis

Engineering Acoustics

University of Quebec at Chicoutimi

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding

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 R_w 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 L_n,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.

Original language	English
Title of host publication	Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering
Editors	Tyler Dare, Stuart Bolton, Patricia Davies, Yutong Xue, Gordon Ebbitt
Publisher	The Institute of Noise Control Engineering of the USA, Inc.
ISBN (Electronic)	9781732598652
DOIs	https://doi.org/10.3397/IN-2021-2619
Publication status	Published - 2021
Event	50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021 - Washington, United States Duration: 2021 Aug 1 → 2021 Aug 5

Publication series

Name	Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering
ISSN (Print)	0736-2935

Conference

Conference	50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021
Country/Territory	United States
City	Washington
Period	2021/08/01 → 2021/08/05

Subject classification (UKÄ)

Fluid Mechanics and Acoustics

Free keywords

Airborne sound
Building acoustics
Impact sound
Neural networks
Prediction model

Access to Document

10.3397/IN-2021-2619

Cite this

Bader Eddin, M., Menard, S., Bard, D., Kouyoumji, J. L., & Vardaxis, N. G. (2021). A sound insulation prediction model for floor structures in wooden buildings using neural networks approach. In T. Dare, S. Bolton, P. Davies, Y. Xue, & G. Ebbitt (Eds.), Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering (Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering). The Institute of Noise Control Engineering of the USA, Inc.. https://doi.org/10.3397/IN-2021-2619

Bader Eddin, Mohamad ; Menard, Sylvain ; Bard, Delphine et al. / A sound insulation prediction model for floor structures in wooden buildings using neural networks approach. Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. editor / Tyler Dare ; Stuart Bolton ; Patricia Davies ; Yutong Xue ; Gordon Ebbitt. The Institute of Noise Control Engineering of the USA, Inc., 2021. (Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering).

@inproceedings{1f8834cce4c8405cb16cbee2b79eefde,

title = "A sound insulation prediction model for floor structures in wooden buildings using neural networks approach",

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.",

keywords = "Airborne sound, Building acoustics, Impact sound, Neural networks, Prediction model",

author = "{Bader Eddin}, Mohamad and Sylvain Menard and Delphine Bard and Kouyoumji, {Jean Luc} and Vardaxis, {Nikolas Georgios}",

note = "Publisher Copyright: {\textcopyright} INTER-NOISE 2021 .All right reserved.; 50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021 ; Conference date: 01-08-2021 Through 05-08-2021",

year = "2021",

doi = "10.3397/IN-2021-2619",

language = "English",

series = "Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering",

publisher = "The Institute of Noise Control Engineering of the USA, Inc.",

editor = "Tyler Dare and Stuart Bolton and Patricia Davies and Yutong Xue and Gordon Ebbitt",

booktitle = "Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering",

}

Bader Eddin, M, Menard, S, Bard, D, Kouyoumji, JL & Vardaxis, NG 2021, A sound insulation prediction model for floor structures in wooden buildings using neural networks approach. in T Dare, S Bolton, P Davies, Y Xue & G Ebbitt (eds), Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering, The Institute of Noise Control Engineering of the USA, Inc., 50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021, Washington, United States, 2021/08/01. https://doi.org/10.3397/IN-2021-2619

A sound insulation prediction model for floor structures in wooden buildings using neural networks approach. / Bader Eddin, Mohamad; Menard, Sylvain; Bard, Delphine et al.
Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. ed. / Tyler Dare; Stuart Bolton; Patricia Davies; Yutong Xue; Gordon Ebbitt. The Institute of Noise Control Engineering of the USA, Inc., 2021. (Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding

TY - GEN

T1 - A sound insulation prediction model for floor structures in wooden buildings using neural networks approach

AU - Bader Eddin, Mohamad

AU - Menard, Sylvain

AU - Bard, Delphine

AU - Kouyoumji, Jean Luc

AU - Vardaxis, Nikolas Georgios

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Airborne sound

KW - Building acoustics

KW - Impact sound

KW - Neural networks

KW - Prediction model

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U2 - 10.3397/IN-2021-2619

DO - 10.3397/IN-2021-2619

M3 - Paper in conference proceeding

AN - SCOPUS:85117367817

T3 - Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering

BT - Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering

A2 - Dare, Tyler

A2 - Bolton, Stuart

A2 - Davies, Patricia

A2 - Xue, Yutong

A2 - Ebbitt, Gordon

PB - The Institute of Noise Control Engineering of the USA, Inc.

T2 - 50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021

Y2 - 1 August 2021 through 5 August 2021

ER -

Bader Eddin M, Menard S, Bard D, Kouyoumji JL, Vardaxis NG. A sound insulation prediction model for floor structures in wooden buildings using neural networks approach. In Dare T, Bolton S, Davies P, Xue Y, Ebbitt G, editors, Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering. The Institute of Noise Control Engineering of the USA, Inc. 2021. (Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering). doi: 10.3397/IN-2021-2619

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

Abstract

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Subject classification (UKÄ)

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