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Sound insulation of...
Sound insulation of lightweight wooden floor structures : ANN model and sensitivity analysis
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- Eddin, Mohamad Bader (author)
- University of Quebec at Chicoutimi
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- Ménard, Sylvain (author)
- University of Quebec at Chicoutimi
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- Bard, Delphine (author)
- 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 (author)
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- Vardaxis, Nikolaos Georgios (author)
- 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
- 2022
- English.
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In: Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. - 9781906913427
- Related links:
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https://lup.lub.lu.s...
Abstract
Subject headings
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- The study aims to develop an artificial neural networks (ANN) model to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed using 252 standardized laboratory measurement curves in one-third octave bands (50 − 5000 Hz). Each floor structure has been divided into three parts in the database: upper, main and ceiling parts. Physical and geometric characteristics (materials, thickness, density, dimensions, mass, and more) are used as network parameters. The results demonstrated that the predictive ability of the model is satisfactory. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum error of 2 dB. However, it is increased up to 5 dB in the worst case prediction of the weighted normalized impact sound pressure level Ln,w. A sensitivity analysis explored the essential parameters on sound insulation estimation. The thickness and the density of upper and main parts of the floors seem to affect estimations the most in all frequencies. In addition, no remarkable attribution has been found for the thickness and density of the ceiling part of the structures.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
Keyword
- artificial neural networks
- prediction model
- sensitivity analysis
- sound insulation
Publication and Content Type
- kon (subject category)
- ref (subject category)
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