Prediction of Sound Insulation Using Artificial Neural Networks—Part II : Lightweight Wooden Façade Structures
(2022) In Applied Sciences (Switzerland) 12(14).- Abstract
A prediction model based on artificial neural networks is adapted to forecast the acoustic performance of airborne sound insulation of various lightweight wooden façade walls. A total of 100 insulation curves were used to develop the prediction model. The data are laboratory measurements of façade walls in one-third-octave bands (50 Hz–5 kHz). For each façade wall, geometric and physical information (material type, dimensions, thicknesses, densities, and more) are used as input parameters. The model shows a satisfactory predictive capability for airborne sound reduction. A higher accuracy is obtained at middle frequencies (250 Hz–1 kHz), while lower and higher frequency ranges often show higher deviations. The weighted airborne sound... (More)
A prediction model based on artificial neural networks is adapted to forecast the acoustic performance of airborne sound insulation of various lightweight wooden façade walls. A total of 100 insulation curves were used to develop the prediction model. The data are laboratory measurements of façade walls in one-third-octave bands (50 Hz–5 kHz). For each façade wall, geometric and physical information (material type, dimensions, thicknesses, densities, and more) are used as input parameters. The model shows a satisfactory predictive capability for airborne sound reduction. A higher accuracy is obtained at middle frequencies (250 Hz–1 kHz), while lower and higher frequency ranges often show higher deviations. The weighted airborne sound reduction index ((Formula presented.)) of façades can be estimated with a maximum difference of 3 dB. Sometimes, the model shows high variations within fundamental and critical frequencies that influence the predictive precision. A sensitivity analysis is implemented to investigate the significance of parameters in insulation estimations. The material density (i.e., cross-laminated timber panel, gypsum board), thickness of the insulation materials, thickness and spacing between interior studs and the total density of façades are factors of significant weight on predictions. The results also emphasize the importance of façade thickness and the total density of the clustered exterior layers.
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- author
- Bader Eddin, Mohamad ; Vardaxis, Nikolaos Georgios LU ; Ménard, Sylvain ; Bard Hagberg, Delphine LU and Kouyoumji, Jean Luc
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- airborne sound insulation, artificial neural networks, façade, prediction model
- in
- Applied Sciences (Switzerland)
- volume
- 12
- issue
- 14
- article number
- 6983
- publisher
- MDPI AG
- external identifiers
-
- scopus:85137331447
- ISSN
- 2076-3417
- DOI
- 10.3390/app12146983
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This research was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada through its IRC and CRD programs (IRCPJ 461745-18 and RDCPJ 524504-18), the Region Nouvelle-Aquitaine (ref. 2017-1R10223) and the industrial partners of the NSERC industrial chair on eco-responsible wood construction (CIRCERB). Publisher Copyright: © 2022 by the authors.
- id
- 4f06e838-bc66-44f8-aae5-ceabc5b5936c
- date added to LUP
- 2022-11-29 14:46:15
- date last changed
- 2023-10-06 12:56:54
@article{4f06e838-bc66-44f8-aae5-ceabc5b5936c,
abstract = {{<p>A prediction model based on artificial neural networks is adapted to forecast the acoustic performance of airborne sound insulation of various lightweight wooden façade walls. A total of 100 insulation curves were used to develop the prediction model. The data are laboratory measurements of façade walls in one-third-octave bands (50 Hz–5 kHz). For each façade wall, geometric and physical information (material type, dimensions, thicknesses, densities, and more) are used as input parameters. The model shows a satisfactory predictive capability for airborne sound reduction. A higher accuracy is obtained at middle frequencies (250 Hz–1 kHz), while lower and higher frequency ranges often show higher deviations. The weighted airborne sound reduction index ((Formula presented.)) of façades can be estimated with a maximum difference of 3 dB. Sometimes, the model shows high variations within fundamental and critical frequencies that influence the predictive precision. A sensitivity analysis is implemented to investigate the significance of parameters in insulation estimations. The material density (i.e., cross-laminated timber panel, gypsum board), thickness of the insulation materials, thickness and spacing between interior studs and the total density of façades are factors of significant weight on predictions. The results also emphasize the importance of façade thickness and the total density of the clustered exterior layers.</p>}},
author = {{Bader Eddin, Mohamad and Vardaxis, Nikolaos Georgios and Ménard, Sylvain and Bard Hagberg, Delphine and Kouyoumji, Jean Luc}},
issn = {{2076-3417}},
keywords = {{airborne sound insulation; artificial neural networks; façade; prediction model}},
language = {{eng}},
number = {{14}},
publisher = {{MDPI AG}},
series = {{Applied Sciences (Switzerland)}},
title = {{Prediction of Sound Insulation Using Artificial Neural Networks—Part II : Lightweight Wooden Façade Structures}},
url = {{http://dx.doi.org/10.3390/app12146983}},
doi = {{10.3390/app12146983}},
volume = {{12}},
year = {{2022}},
}