Mustafa TOSUN, Kevser DİNCER


In this paper, analysis of sound transmission losses through lightweight concrete walls was conducted against the high way trafic noises. The walls are generally used for thermal insulation purposes in Turkey. Sound transmission was modeled using ANN. Input parameters frequency, density of lightweight concrete wall and thickness of lightweight concrete wall structure (f, M, d2) and output parameter TS were described.

When the outcomes of the TS analysis and those of ANN modeling are summarized together; Sound transmission losses improve with higher frequencies, higher wall densities and increased wall cross sections. Regardless of sufficient thermal insulation of single layered lightweight concrete walls as stipulated by the Turkey Institute of Standards (TSE 825), the wall cross sections were found to be insufficient in terms of sound transmission. Beside thermal insulation of the single layered lightweight concrete walls’ regulations, it was found with this study that, it is also necessary to analyze sound transmission lossess, after which the wall cross sections should be sized.

Anahtar Kelimeler

Artificial neuron network (ANN), lightweight concrete wall, sound transmit loss.

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Ballagh K.O. Accuracy of prediction methods for sound transmission los, inter-noise 2004, The 33rd International Congress and Exposition on Noise Control Engineering. New Zealand; 2004.

Vigran T.E. Predicting the sound reduction index of finite size specimen by a simplified spatial windowing technique, Journal of Sound and Vibration 2009; 325: 507–512.

Julien L, Noureddine A. Numerical and experimental investigation of the effect of structural links on the sound transmission of a lightweight double panel structure, Journal of Sound and Vibration 2009; 324:712–732.

Wang J, Lu T.J, Woodhouse J, Langley R.S, Evans J. Sound transmission through lightweight double-leaf partitions: theoretical modelling, Journal of Sound and Vibration 2005; 286: 817–847.

Matsumoto T, Uchida M, Sugaya H, Tachibana H. Development of multiple drywall with high sound insulation performance, Applied Acoustics 2006; 71:595-608.

Bao C, Pan J. Experimental study of different approaches for active control of sound transmission through double walls, Journal of Acoustical Society of America 1997; 102: 1664-1670.

Jeona J.Y, Ryu J. K, Leea P. J. A quantification model of overall dissatisfaction with indoor noise environment in residential buildings, Applied Acoustics 2010; 71: 914-921.

Oldhama D.J, Mohsen E.A. A model investigation of the acoustical performance of courtyard houses with respect to noise from road traffic, Applied Acoustics 2003; 12: 215-230.

Kalogirou S.A. Artificial intelligence for the modeling and control of combustion processes: a review, Progress in Energy and Combustion Science 2003; 29: 515-566.

Zhang C.L. Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using artificial neural network, International Journal of Refrigeration 2005; 28:506-514.

Tosun M, Dincer K. Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey, International Journal of Refrigeration 2011; 34:362-373.

Safa M, Samarasinghe S. Modelling fuel consumption in wheat production using artificial neural networks, Energy 2013; 49: 337–343.

Olanrewaju O.A, Jimoh A. A, Kholopane P.A. Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors, Energy 2012; 46:629–635.

Kocabas F, Korkmaz M, Sorgucu U, Donmez S. Modeling of heating and cooling performance of counter flow type vortex tube by using artificial neural network, International Journal of Refrigeration 2010; 33: 963-972.

Kumar M.M, Stoll N, Stoll R. An Energy-Gain Bounding Approach to Robust Fuzzy Identification, Automatica 2006; 42:711-721.

TS 825. Thermal Insulation Requirements for Buildings; Ankara; Turkey; 2008.

Özer M. Yapı Akustği ve Ses Yalıtımı. Istanbul; Turkey: Arpaz Publication pp.143.; 1979.

Kalogirou S.A, Bojic M. Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy 2000; 25: 479–491.

Yang J, Rivard H, Zmeureanu R. On-line building energy prediction using adaptive artificial neural Networks, Energy and Buildings 2005; 37:1250–1259. (Accessed 05 May 2012).s

Beranek L.L, Ver I. L. Noise and Vibration Control Engineering. Principles and Aplications. New York: A Wiley-Interscience Publication; 1992 p. 633.

Croome D. J. Noise and the Design of Buildings and Services. New York: Construction Press;1992 p. 31.

Kalogirou S.A. Applications of artificial neural-networks for energy systems, Applied Energy 2000; 67:17–35.

Sözen A, Arcaklioglu E. Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach, Applied Thermal Engineering 2007; 27: 481-491.

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