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2017 | Vol. 42, No. 4 | 643--651
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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%.
Wydawca

Rocznik
Strony
643--651
Opis fizyczny
Bibliogr. 60 poz., rys., tab., wykr.
Twórcy
autor
autor
  • National Institute of Technology, Hamirpur, 1601 Hamirpur – 177 005, India
autor
  • National Institute of Technology, Kurukshetra, Kurukshetra – 136 119, India
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-45698699-ba7b-4bca-b037-f32733d23cce
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