Sound transmission loss of gypsum wallboard partitions. Report ♯2. Steel stud partitions having cavities filled with glass fiber batts

1982 ◽  
Vol 71 (4) ◽  
pp. 902-907 ◽  
Author(s):  
David W. Green ◽  
Cameron W. Sherry
Keyword(s):  
Glass Fiber ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Sound Transmission Loss ◽  
Steel Stud

scholarly journals Analysis of the transmission loss of double-leaf panels with an equivalent spring–mass model for studs

2020 ◽  
Vol 37 ◽  
pp. 126-133
Author(s):  
Yuan-Wei Li ◽  
Chao-Nan Wang
Keyword(s):  
Distribution Curve ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Experimental Results ◽  
Sound Insulation ◽  
Sound Transmission Loss ◽  
Mass Model ◽  
Steel Stud ◽  
Panel Thickness ◽  
Stiffness Distribution

Abstract The purpose of this study was to investigate the sound insulation of double-leaf panels. In practice, double-leaf panels require a stud between two surface panels. To simplify the analysis, a stud was modeled as a spring and mass. Studies have indicated that the stiffness of the equivalent spring is not a constant and varies with the frequency of sound. Therefore, a frequency-dependent stiffness curve was used to model the effect of the stud to analyze the sound insulation of a double-leaf panel. First, the sound transmission loss of a panel reported by Halliwell was used to fit the results of this study to determine the stiffness of the distribution curve. With this stiffness distribution of steel stud, some previous proposed panels are also analyzed and are compared to the experimental results in the literature. The agreement is good. Finally, the effects of parameters, such as the thickness and density of the panel, thickness of the stud and spacing of the stud, on the sound insulation of double-leaf panels were analyzed.

Applicability of Artificial Neural Network to Estimate Sound Transmission Loss of Ultrafine Glass Fiber Felts

2019 ◽  
Vol 105 (4) ◽  
pp. 650-656 ◽  
Author(s):  
Fei Wang ◽  
Zhaofeng Chen ◽  
Cao Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Fiber ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Areal Density ◽  
Sound Frequency ◽  
Sound Transmission Loss ◽  
Ann Model ◽  
Artificial Neural

In the present study, the sound transmission loss (STL) of ultrafine glass fiber felts in terms of areal density and sound frequency has been modeled by artificial neural network (ANN), the Law of Theoretic Mass and fitting polynomial, respectively. The STL of ultrafine glass fiber felts with the areal density ranging from 0 to 300 g/m2 and at the sound frequency ranging from 500 to 6300 Hz was employed as training data for ANN. By the optimization of ANN structure, the number of neurons in the two hidden layers was determined to 8 and 4 respectively. The mean squared error of the ANN model was only 0.191 and the correlation coefficient was 0.9989, which showed high accuracy for estimating the STL of the felts. Compared with other two models, the ANN model showed excellent agreement with the measured results and it's very appropriate for the estimation of acoustic properties of ultrafine glass fiber felts.

scholarly journals Parametric Sensitivity Analysis of Factors Affecting Sound Transmission Loss of Multi-Layered Building Elements Using Taguchi Method

2015 ◽  
Vol 39 (2) ◽  
pp. 165-176 ◽  
Author(s):  
Naveen Garg ◽  
Anil Kumar ◽  
Sagar Maji
Keyword(s):  
Taguchi Method ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Relative Importance ◽  
Sound Transmission Loss ◽  
Key Factors ◽  
Factors Affecting ◽  
Concrete Blocks ◽  
Single Number ◽  
Steel Stud

Abstract The paper presents application of Taguchi method in optimizing the sound transmission loss through sandwich gypsum constructions and those comprising of masonry concrete blocks and gypsum boards in order to investigate the relative influence of the various parameters affecting the sound transmission loss. The application of Taguchi method for optimizing sound transmission loss has been rarely reported. The present work uses the results analytically predicted on “Insul” software for various sandwich material configurations as desired by each experimental run in an L8 orthogonal array. The relative importance of the parameters on single-number rating, Rw (C, Ctr) is evaluated in terms of percentage contribution using Analysis of Variance (ANOVA). The ANOVA method reveals that type of studs, steel stud frame and number of gypsum layers attached are the key factors controlling the sound transmission loss characteristics of sandwich multi-layered constructions.

Sound insulation of multi-layer glass-fiber felts: Role of morphology

2016 ◽  
Vol 87 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Yong Yang ◽  
Binbin Li ◽  
Zhaofeng Chen ◽  
Ni Sui ◽  
Zhou Chen ◽  
...  
Keyword(s):  
Glass Fiber ◽  
Transmission Loss ◽  
Resonance Effect ◽  
Sound Transmission ◽  
Areal Density ◽  
Sound Insulation ◽  
Sound Transmission Loss ◽  
Layer Structures ◽  
Air Layers

Glass-fiber felts have emerged as a popular material for noise reduction. This paper investigates the effect of various morphologies (micro-layer, macro-layer and air-layer) of glass-fiber felts on sound insulation. The sound transmission loss is measured by a Brüel & Kjár (B&K) impedance tube. The results show that the sound insulation of glass-fiber felts can be improved by increasing the number of macro-layers. The comparison between the macro- and micro-layer of glass-fiber felts on sound insulation is systematically carried out. Notably, the sound transmission loss of glass-fiber felts with similar areal density and thickness favors macro-layer structures over micro-layer structures. A simple model is established to explain this phenomenon. In addition, the sound transmission loss exhibits period fluctuations due to the presence of the air-layer between glass-fiber felts, which can be theoretically explained by the resonance effect. It is found that sound transmission loss can be improved by increasing the number of air-layers.

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