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Prediction of Sound...
Prediction of Sound Insulation Using Artificial Neural Networks—Part I : LightweightWooden Floor Structures
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- Eddin, Mohamad Bader (författare)
- University of Quebec at Chicoutimi
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- Ménard, Sylvain (författare)
- University of Quebec at Chicoutimi
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- Hagberg, Delphine Bard (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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- 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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(creator_code:org_t)
- 2022-03-02
- 2022
- Engelska 24 s.
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Ingår i: Acoustics. - : MDPI AG. - 2624-599X. ; 4:1, s. 203-226
- Relaterad länk:
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http://dx.doi.org/10... (free)
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visa fler...
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https://www.mdpi.com...
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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
- The artificial neural networks approach is applied to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed based on 252 standardized laboratory measurement curves in one-third octave bands (50-5000 Hz). Physical and geometric characteristics of each floor structure (materials, thickness, density, dimensions, mass and more) are utilized as network parameters. The predictive capability is satisfactory, and the model can estimate airborne sound better than impact sound cases especially in the middle-frequency range (250-1000 Hz), while higher frequency bands often show high errors. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum error of 2 dB. However, the error increased up to 5 dB in the worse case prediction of the weighted normalized impact sound pressure level Ln,w. The model showed high variations near the fundamental and critical frequency areas which affect the accuracy. A feature attribution analysis explored the essential parameters on estimation of sound insulation. The thickness of the insulation materials, the density of cross-laminated timber slab and the concrete floating floors and the total density of floor structures seem to affect predictions the most. A comparison between wet and dry floor solution systems indicated the importance of the upper part of floors to estimate airborne and impact sound in low frequencies.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
Nyckelord
- Airborne sound
- Artificial neural networks
- Impact sound
- Insulation
- Prediction model
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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