A comparison of numerical approaches to quantify sound insulation of lightweight wooden floor structures
(2022) 51st International Congress and Exposition on Noise Control Engineering, Internoise 2022- Abstract
Quantifying air-borne and structure-borne sound insulation is an important design consideration for the indoor comfort in a building. Although sound insulation performance is commonly measured experimentally, numerical methods can have time-saving and economic benefits. Further, numerical methods can be incorporated within building simulations to provide an estimate of the acoustic environment. In response, this paper evaluates three different computational approaches for quantifying sound insulation in one-third octave bands (50 Hz -5 kHz) of a lightweight floor including: an analytical (theoretical) model, a finite element model (FEM), and an artificial neural network (ANN) model. The three numerical methods are tested on the sound... (More)
Quantifying air-borne and structure-borne sound insulation is an important design consideration for the indoor comfort in a building. Although sound insulation performance is commonly measured experimentally, numerical methods can have time-saving and economic benefits. Further, numerical methods can be incorporated within building simulations to provide an estimate of the acoustic environment. In response, this paper evaluates three different computational approaches for quantifying sound insulation in one-third octave bands (50 Hz -5 kHz) of a lightweight floor including: an analytical (theoretical) model, a finite element model (FEM), and an artificial neural network (ANN) model. The three numerical methods are tested on the sound insulation of a cross laminated timber (CLT) floor. The results of this study show that the ANN model is able to accurately predict the air-borne and impact sound insulation performance at frequencies above 250 Hz, but over-predicts the air-borne performance and under-predicts the impact performance at low frequencies. However, the analytical and FEM strategies provide acceptable estimations, useful during the conceptual design stage, but with higher deviations than ANN model across all frequencies. While no model is able to accurately represent acoustic behavior across all frequencies, this work highlights the advantages and disadvantages when applied to predicting the sound insulation of a CLT floor.
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- author
- Eddin, Mohamad Bader ; Broyles, Jonathan M. ; Ménard, Sylvain ; Bard, Delphine LU and Kouyoumji, Jean Luc
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- artificial neural networks, building acoustics, floor structures, numerical 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:85147411328
- ISBN
- 9781906913427
- language
- English
- LU publication?
- yes
- id
- 6c9902bf-88ad-4a3a-ac9d-41b5421f880a
- date added to LUP
- 2023-02-20 15:11:08
- date last changed
- 2023-10-09 13:57:39
@inproceedings{6c9902bf-88ad-4a3a-ac9d-41b5421f880a,
abstract = {{<p>Quantifying air-borne and structure-borne sound insulation is an important design consideration for the indoor comfort in a building. Although sound insulation performance is commonly measured experimentally, numerical methods can have time-saving and economic benefits. Further, numerical methods can be incorporated within building simulations to provide an estimate of the acoustic environment. In response, this paper evaluates three different computational approaches for quantifying sound insulation in one-third octave bands (50 Hz -5 kHz) of a lightweight floor including: an analytical (theoretical) model, a finite element model (FEM), and an artificial neural network (ANN) model. The three numerical methods are tested on the sound insulation of a cross laminated timber (CLT) floor. The results of this study show that the ANN model is able to accurately predict the air-borne and impact sound insulation performance at frequencies above 250 Hz, but over-predicts the air-borne performance and under-predicts the impact performance at low frequencies. However, the analytical and FEM strategies provide acceptable estimations, useful during the conceptual design stage, but with higher deviations than ANN model across all frequencies. While no model is able to accurately represent acoustic behavior across all frequencies, this work highlights the advantages and disadvantages when applied to predicting the sound insulation of a CLT floor.</p>}},
author = {{Eddin, Mohamad Bader and Broyles, Jonathan M. and Ménard, Sylvain and Bard, Delphine and Kouyoumji, Jean Luc}},
booktitle = {{Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering}},
isbn = {{9781906913427}},
keywords = {{artificial neural networks; building acoustics; floor structures; numerical analysis; sound insulation}},
language = {{eng}},
publisher = {{The Institute of Noise Control Engineering of the USA, Inc.}},
title = {{A comparison of numerical approaches to quantify sound insulation of lightweight wooden floor structures}},
year = {{2022}},
}