Garg, Naveen and Dhruw, Siddharth and Gandhi, Laghu (2017) Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks. Archives of Acoustics, 42. pp. 643-651. ISSN 0137-5075

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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 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%.

Item Type:	Article
Additional Information:	copyright for this article belongs to M\S Polish Scientific Publishers.
Subjects:	Acoustics
Divisions:	UNSPECIFIED
Depositing User:	Users 27 not found.
Date Deposited:	07 Dec 2018 08:37
Last Modified:	07 Dec 2018 08:37
URI:	http://npl.csircentral.net/id/eprint/2827

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