Sound insulation of lightweight wooden floor structures : ANN model and sensitivity analysis
(2022) 51st International Congress and Exposition on Noise Control Engineering, Internoise 2022- Abstract
The study aims to develop an artificial neural networks (ANN) model to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed using 252 standardized laboratory measurement curves in one-third octave bands (50 − 5000 Hz). Each floor structure has been divided into three parts in the database: upper, main and ceiling parts. Physical and geometric characteristics (materials, thickness, density, dimensions, mass, and more) are used as network parameters. The results demonstrated that the predictive ability of the model is satisfactory. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum... (More)
The study aims to develop an artificial neural networks (ANN) model to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed using 252 standardized laboratory measurement curves in one-third octave bands (50 − 5000 Hz). Each floor structure has been divided into three parts in the database: upper, main and ceiling parts. Physical and geometric characteristics (materials, thickness, density, dimensions, mass, and more) are used as network parameters. The results demonstrated that the predictive ability of the model is satisfactory. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum error of 2 dB. However, it is increased up to 5 dB in the worst case prediction of the weighted normalized impact sound pressure level Ln,w. A sensitivity analysis explored the essential parameters on sound insulation estimation. The thickness and the density of upper and main parts of the floors seem to affect estimations the most in all frequencies. In addition, no remarkable attribution has been found for the thickness and density of the ceiling part of the structures.
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- author
- Eddin, Mohamad Bader ; Ménard, Sylvain ; Bard, Delphine LU ; Kouyoumji, Jean Luc and Vardaxis, Nikolaos Georgios LU
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- artificial neural networks, prediction model, sensitivity analysis, sound insulation
- host publication
- Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering
- publisher
- The Institute of Noise Control Engineering of the USA, Inc.
- conference name
- 51st International Congress and Exposition on Noise Control Engineering, Internoise 2022
- conference location
- Glasgow, United Kingdom
- conference dates
- 2022-08-21 - 2022-08-24
- external identifiers
-
- scopus:85147441053
- ISBN
- 9781906913427
- language
- English
- LU publication?
- yes
- id
- fa759bdb-9a36-476a-bcf5-d5bf2919b03f
- date added to LUP
- 2023-02-20 15:09:25
- date last changed
- 2023-10-09 13:57:17
@inproceedings{fa759bdb-9a36-476a-bcf5-d5bf2919b03f,
abstract = {{<p>The study aims to develop an artificial neural networks (ANN) model to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed using 252 standardized laboratory measurement curves in one-third octave bands (50 − 5000 Hz). Each floor structure has been divided into three parts in the database: upper, main and ceiling parts. Physical and geometric characteristics (materials, thickness, density, dimensions, mass, and more) are used as network parameters. The results demonstrated that the predictive ability of the model is satisfactory. The forecast of the weighted airborne sound reduction index R<sub>w</sub> was calculated with a maximum error of 2 dB. However, it is increased up to 5 dB in the worst case prediction of the weighted normalized impact sound pressure level L<sub>n,w</sub>. A sensitivity analysis explored the essential parameters on sound insulation estimation. The thickness and the density of upper and main parts of the floors seem to affect estimations the most in all frequencies. In addition, no remarkable attribution has been found for the thickness and density of the ceiling part of the structures.</p>}},
author = {{Eddin, Mohamad Bader and Ménard, Sylvain and Bard, Delphine and Kouyoumji, Jean Luc and Vardaxis, Nikolaos Georgios}},
booktitle = {{Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering}},
isbn = {{9781906913427}},
keywords = {{artificial neural networks; prediction model; sensitivity analysis; sound insulation}},
language = {{eng}},
publisher = {{The Institute of Noise Control Engineering of the USA, Inc.}},
title = {{Sound insulation of lightweight wooden floor structures : ANN model and sensitivity analysis}},
year = {{2022}},
}