- Tytuł:
- Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks
- Autorzy:
-
Garg, N.
Dhruw, S.
Gandhi, L. - Powiązania:
- https://bibliotekanauki.pl/articles/176461.pdf
- Data publikacji:
- 2017
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
weighted sound reduction index
Rw
Sound Transmission Class
STC - Opis:
- The paper presents the application of Artificial Neural Networks (ANN) in predicting sound insulation through multi-layered sandwich gypsum partition panels. The objective of the work is to develop an Artificial Neural Network (ANN) model to estimate the Rw and STC value of sandwich gypsum constructions. The experimental results reported by National Research Council, Canada for Gypsum board walls (Halliwell et al., 1998) were utilized to develop the model. A multilayer feed-forward approach comprising of 13 input parameters was developed for predicting the Rw and STC value of sandwich gypsum constructions. The Levenberg-Marquardt optimization technique has been used to update the weights in back-propagation algorithm. The presented approach could be very useful for design and optimization of acoustic performance of new sandwich partition panels providing higher sound insulation. The developed ANN model shows a prediction error of ± 3 dB or points with a confidence level higher than 95%.
- Źródło:
-
Archives of Acoustics; 2017, 42, 4; 643-651
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł