Research Article
BibTex RIS Cite

Hafif Beton Duvarlarda Ses İletim Kaybının Belirlenmesi ve Yapay Sinir Ağının Modellenmesi

Year 2018, Volume: 6 Issue: 3, 461 - 477, 01.09.2018

Mustafa Tosun Kevser Dincer

https://doi.org/10.15317/Scitech.2018.145

Abstract

Bu makalede, ana yolu trafik gürültüsüne karşı hafif beton duvarlardan ses iletim kayıplarının analizi yapılmıştır. Duvarlar genellikle Türkiye'de ısı yalıtım amacıyla kullanılmaktadır. Ses iletimi ANN kullanılarak modellenmiştir. Giriş parametreleri frekans, hafif beton duvarın yoğunluğu ve hafif beton duvar yapısının kalınlığı (f, M, d2) ve çıkış parametresi TS tanımlanmıştır. TS analizinin sonuçları ve modelleme sonuçları birlikte özetlendiğinde; ses iletimi kayıpları daha yüksek frekanslar, daha yüksek duvar yoğunlukları ve artan duvar kesitleri ile gelişir. Türkiye standart Enstitüsü (TSE 825) tarafından öngörülen tek katmanlı hafif beton duvarların yeterli ısı yalıtımına bakılmaksızın, duvar kesitlerinin ses iletimi açısından yetersiz olduğu bulunmuştur. Tek katmanlı hafif beton duvarların düzenlemelerinin ısı yalıtımının yanı sıra, bu çalışmada, ses iletimi kayıplarını analiz etmek için de gerekli olduğu ve daha sonra duvar kesitlerinin boyutlandırılması gerektiği bulunmuştur.

Keywords

Yapay sinir ağı Hafif beton duvar Ses iletim kaybı

References

  • 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.
  • Bao, C., Pan, J., 1997, “Experimental Study of Different Approaches for Active Control of Sound Transmission Through Double Walls”, Journal of Acoustical Society of America, Vol. 102, pp. 1664-1670.
  • Beranek, L.L., Ver, I. L., 1992, Noise and Vibration Control Engineering Principles and Aplications. A Wiley-Interscience Publication, New York, p. 633.
  • Croome, D. J., 1992, Noise and the Design of Buildings and Services, Construction Press, New York, p. 31.
  • Jeona, J.Y., Ryu, J. K, Leea, P. J., 2010, “A Quantification Model of Overall Dissatisfaction with Indoor Noise Environment İn Residential Buildings”, Applied Acoustics, Vol. 71, pp. 914-921. 3]
  • Julien, L., Noureddine, A., 2009, “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, Vol. 324, pp. 712–732.
  • Kalogirou, S.A., Bojic, M., 2000, “Artificial Neural Networks for the Prediction of The Energy Consumption of a Passive Solar Building”, Energy, Vol. 25, pp. 479–491.
  • Kalogirou, S.A., 2000, “Applications of Artificial Neural-networks for Energy Systems”, Applied Energy, Vol. 67, pp. 17–35.
  • Kalogirou, S.A., 2003, “Artificial Intelligence for the Modeling and Control of Combustion Processes: a Review”, Progress in Energy and Combustion Science, Vol. 29, pp. 515-566.
  • Kocabas, F., Korkmaz, M., Sorgucu, U., Donmez, S., 2010, “Modeling Of Heating And Cooling Performance of Counter Flow Type Vortex Tube by Using Artificial Neural Network”, International Journal of Refrigeration, Vol. 33, pp. 963-972.
  • Özer, M., 1979, Yapı Akustği ve Ses Yalıtım, Arpaz Publication, Istanbul, Turkey, pp.143.
  • Kumar, M.M., Stoll, N., Stoll, R., 2006, “An Energy-Gain Bounding Approach to Robust Fuzzy Identification”, Automatica, Vol. 42, pp. 711-721.
  • Matsumoto, T., Uchida, M., Sugaya, H., Tachibana, H., 2006, “Development of Multiple Drywall with High Sound Insulation Performance”, Applied Acoustics, Vol. 71, pp. 595-608.13].
  • Olanrewaju, O.A., Jimoh, A.A., Kholopane, P.A., 2012, “Integrated IDA–ANN–DEA for Assessment and Optimization of Energy Consumption in İndustrial Sectors”, Energy, Vol. 46, pp. 629–635.
  • Oldhama, D.J, Mohsen, E.A., 2003, “A Model Investigation of the Acoustical Performance of Courtyard Houses with Respect to Noise from Road Traffic”, Applied Acoustics, Vol. 12, pp. 215-230.
  • Sözen, A., Arcaklioglu, E., 2007, “Exergy Analysis of an Ejector-Absorption Heat Transformer Using Artificial Neural Network Approach”, Applied Thermal Engineering, Vol. 27, 481-491.
  • Safa, M., Samarasinghe, S., 2013, “Modelling Fuel Consumption in Wheat Production Using Artificial Neural Networks”, Energy, Vol. 49, pp. 337–343.
  • Tosun, M., Dincer, K., 2011, “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, Vol. 34, pp. 362-373.
  • TS 825, Thermal Insulation Requirements for Buildings, Ankara, Turkey, 2008.
  • Vigran, T.E., 2009, “Predicting the Sound Reduction Index of Finite Size Specimen by a Simplified Spatial Windowing Technique”, Journal of Sound and Vibration, Vol. 325, pp. 507–512.
  • Yang, J., Rivard, H., Zmeureanu, R., 2005, “On-line Building Energy Prediction Using Adaptive Artificial Neural Networks”, Energy and Buildings , Vol. 37, pp. 1250–1259.
  • Wang, J., Lu, T.J, Woodhouse, J., Langley, R.S, Evans, J., 2005, “Sound Transmission Through Lightweight Double-Leaf Partitions: Theoretical Modelling”, Journal of Sound and Vibration, Vol. 286, pp. 817–847.
  • WHO, http://www.euro.who.int/en/who-we-are/policy-documents (Accessed 05 May 2012).
  • Zhang, C.L., 2005, “Generalized Correlation of Refrigerant Mass Flow Rate Through Adiabatic Capillary Tubes using Artificial Neural Network”, International Journal of Refrigeration, Vol. 28, pp. 506-514.

DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK

Year 2018, Volume: 6 Issue: 3, 461 - 477, 01.09.2018

Mustafa Tosun Kevser Dincer

https://doi.org/10.15317/Scitech.2018.145

Abstract

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.

Keywords

Artificial neuron network (ANN) Lightweight concrete wall Sound transmit loss

References

  • 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.
  • Bao, C., Pan, J., 1997, “Experimental Study of Different Approaches for Active Control of Sound Transmission Through Double Walls”, Journal of Acoustical Society of America, Vol. 102, pp. 1664-1670.
  • Beranek, L.L., Ver, I. L., 1992, Noise and Vibration Control Engineering Principles and Aplications. A Wiley-Interscience Publication, New York, p. 633.
  • Croome, D. J., 1992, Noise and the Design of Buildings and Services, Construction Press, New York, p. 31.
  • Jeona, J.Y., Ryu, J. K, Leea, P. J., 2010, “A Quantification Model of Overall Dissatisfaction with Indoor Noise Environment İn Residential Buildings”, Applied Acoustics, Vol. 71, pp. 914-921. 3]
  • Julien, L., Noureddine, A., 2009, “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, Vol. 324, pp. 712–732.
  • Kalogirou, S.A., Bojic, M., 2000, “Artificial Neural Networks for the Prediction of The Energy Consumption of a Passive Solar Building”, Energy, Vol. 25, pp. 479–491.
  • Kalogirou, S.A., 2000, “Applications of Artificial Neural-networks for Energy Systems”, Applied Energy, Vol. 67, pp. 17–35.
  • Kalogirou, S.A., 2003, “Artificial Intelligence for the Modeling and Control of Combustion Processes: a Review”, Progress in Energy and Combustion Science, Vol. 29, pp. 515-566.
  • Kocabas, F., Korkmaz, M., Sorgucu, U., Donmez, S., 2010, “Modeling Of Heating And Cooling Performance of Counter Flow Type Vortex Tube by Using Artificial Neural Network”, International Journal of Refrigeration, Vol. 33, pp. 963-972.
  • Özer, M., 1979, Yapı Akustği ve Ses Yalıtım, Arpaz Publication, Istanbul, Turkey, pp.143.
  • Kumar, M.M., Stoll, N., Stoll, R., 2006, “An Energy-Gain Bounding Approach to Robust Fuzzy Identification”, Automatica, Vol. 42, pp. 711-721.
  • Matsumoto, T., Uchida, M., Sugaya, H., Tachibana, H., 2006, “Development of Multiple Drywall with High Sound Insulation Performance”, Applied Acoustics, Vol. 71, pp. 595-608.13].
  • Olanrewaju, O.A., Jimoh, A.A., Kholopane, P.A., 2012, “Integrated IDA–ANN–DEA for Assessment and Optimization of Energy Consumption in İndustrial Sectors”, Energy, Vol. 46, pp. 629–635.
  • Oldhama, D.J, Mohsen, E.A., 2003, “A Model Investigation of the Acoustical Performance of Courtyard Houses with Respect to Noise from Road Traffic”, Applied Acoustics, Vol. 12, pp. 215-230.
  • Sözen, A., Arcaklioglu, E., 2007, “Exergy Analysis of an Ejector-Absorption Heat Transformer Using Artificial Neural Network Approach”, Applied Thermal Engineering, Vol. 27, 481-491.
  • Safa, M., Samarasinghe, S., 2013, “Modelling Fuel Consumption in Wheat Production Using Artificial Neural Networks”, Energy, Vol. 49, pp. 337–343.
  • Tosun, M., Dincer, K., 2011, “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, Vol. 34, pp. 362-373.
  • TS 825, Thermal Insulation Requirements for Buildings, Ankara, Turkey, 2008.
  • Vigran, T.E., 2009, “Predicting the Sound Reduction Index of Finite Size Specimen by a Simplified Spatial Windowing Technique”, Journal of Sound and Vibration, Vol. 325, pp. 507–512.
  • Yang, J., Rivard, H., Zmeureanu, R., 2005, “On-line Building Energy Prediction Using Adaptive Artificial Neural Networks”, Energy and Buildings , Vol. 37, pp. 1250–1259.
  • Wang, J., Lu, T.J, Woodhouse, J., Langley, R.S, Evans, J., 2005, “Sound Transmission Through Lightweight Double-Leaf Partitions: Theoretical Modelling”, Journal of Sound and Vibration, Vol. 286, pp. 817–847.
  • WHO, http://www.euro.who.int/en/who-we-are/policy-documents (Accessed 05 May 2012).
  • Zhang, C.L., 2005, “Generalized Correlation of Refrigerant Mass Flow Rate Through Adiabatic Capillary Tubes using Artificial Neural Network”, International Journal of Refrigeration, Vol. 28, pp. 506-514.
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mustafa Tosun This is me

Kevser Dincer

Publication Date September 1, 2018
Published in Issue Year 2018 Volume: 6 Issue: 3

Cite

APA Tosun, M., & Dincer, K. (2018). DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 6(3), 461-477. https://doi.org/10.15317/Scitech.2018.145
AMA Tosun M, Dincer K. DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK. sujest. September 2018;6(3):461-477. doi:10.15317/Scitech.2018.145
Chicago Tosun, Mustafa, and Kevser Dincer. “DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6, no. 3 (September 2018): 461-77. https://doi.org/10.15317/Scitech.2018.145.
EndNote Tosun M, Dincer K (September 1, 2018) DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6 3 461–477.
IEEE M. Tosun and K. Dincer, “DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK”, sujest, vol. 6, no. 3, pp. 461–477, 2018, doi: 10.15317/Scitech.2018.145.
ISNAD Tosun, Mustafa - Dincer, Kevser. “DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6/3 (September 2018), 461-477. https://doi.org/10.15317/Scitech.2018.145.
JAMA Tosun M, Dincer K. DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK. sujest. 2018;6:461–477.
MLA Tosun, Mustafa and Kevser Dincer. “DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, vol. 6, no. 3, 2018, pp. 461-77, doi:10.15317/Scitech.2018.145.
Vancouver Tosun M, Dincer K. DETERMINATION OF SOUND TRANSMISSION LOSS IN LIGHTWEIGHT CONCRETE WALLS AND MODELING ARTIFICIAL NEURAL NETWORK. sujest. 2018;6(3):461-77.

MAKALELERINIZI 

http://sujest.selcuk.edu.tr

uzerinden gonderiniz