Details

Title

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

Journal title

Archives of Acoustics

Yearbook

2017

Numer

No 4

Publication authors

Keywords

weighted sound reduction index, Rw; Sound Transmission Class, STC

Divisions of PAS

Nauki Techniczne

Abstract

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%.

Publisher

Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society

Identifier

ISSN 0137-5075 ; eISSN 2300-262X

DOI

10.1515/aoa-2017-0068

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