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Prediction of Sound...
Prediction of Sound Insulation Using Artificial Neural Networks—Part II : Lightweight Wooden Façade Structures
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- Bader Eddin, Mohamad (författare)
- University of Quebec at Chicoutimi
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- Vardaxis, Nikolaos Georgios (författare)
- Lund University,Lunds universitet,Teknisk akustik,Institutionen för byggvetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,Engineering Acoustics,Department of Construction Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Ménard, Sylvain (författare)
- University of Quebec at Chicoutimi
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- Bard Hagberg, Delphine (författare)
- Lund University,Lunds universitet,Teknisk akustik,Institutionen för byggvetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: Cirkulär byggindustri,LTH profilområden,Engineering Acoustics,Department of Construction Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Circular Building Sector,LTH Profile areas,Faculty of Engineering, LTH
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- Kouyoumji, Jean Luc (författare)
- FCBA Technical Institute
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(creator_code:org_t)
- 2022-07-10
- 2022
- Engelska.
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Ingår i: Applied Sciences (Switzerland). - : MDPI AG. - 2076-3417. ; 12:14
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- A prediction model based on artificial neural networks is adapted to forecast the acoustic performance of airborne sound insulation of various lightweight wooden façade walls. A total of 100 insulation curves were used to develop the prediction model. The data are laboratory measurements of façade walls in one-third-octave bands (50 Hz–5 kHz). For each façade wall, geometric and physical information (material type, dimensions, thicknesses, densities, and more) are used as input parameters. The model shows a satisfactory predictive capability for airborne sound reduction. A higher accuracy is obtained at middle frequencies (250 Hz–1 kHz), while lower and higher frequency ranges often show higher deviations. The weighted airborne sound reduction index ((Formula presented.)) of façades can be estimated with a maximum difference of 3 dB. Sometimes, the model shows high variations within fundamental and critical frequencies that influence the predictive precision. A sensitivity analysis is implemented to investigate the significance of parameters in insulation estimations. The material density (i.e., cross-laminated timber panel, gypsum board), thickness of the insulation materials, thickness and spacing between interior studs and the total density of façades are factors of significant weight on predictions. The results also emphasize the importance of façade thickness and the total density of the clustered exterior layers.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Husbyggnad (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Building Technologies (hsv//eng)
Nyckelord
- airborne sound insulation
- artificial neural networks
- façade
- prediction model
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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