Archives of Acoustics, 42, 4, pp. 643–651, 2017

Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks

Naveen GARG
CSIR-National Physical Laboratory

Siddharth DHRUW
National Institute of Technology, Hamirpur

National Institute of Technology, Kurukshetra

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 $R_w$ 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 $R_w$ 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%.
Keywords: weighted sound reduction index; Rw; Sound Transmission Class; STC
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).


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DOI: 10.1515/aoa-2017-0068