Prediction of Sound Insulation Using Artificial Neural Networks—Part I : LightweightWooden Floor Structures

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Eddin, Mohamad BaderUniversity of Quebec at Chicoutimi (author)

Prediction of Sound Insulation Using Artificial Neural Networks—Part I : LightweightWooden Floor Structures

Article/chapterEnglish2022

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2022-03-02
MDPI AG,2022
24 s.

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LIBRIS-ID:oai:lup.lub.lu.se:fa2c5008-2496-453b-9f68-dfd783950bf2
https://lup.lub.lu.se/record/fa2c5008-2496-453b-9f68-dfd783950bf2URI
https://doi.org/10.3390/acoustics4010013DOI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype
Subject category:ref swepub-contenttype

Notes

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.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Maskinteknik Strömningsmekanik och akustik hsv//swe
ENGINEERING AND TECHNOLOGY Mechanical Engineering Fluid Mechanics and Acoustics hsv//eng
Airborne sound
Artificial neural networks
Impact sound
Insulation
Prediction model

Added entries (persons, corporate bodies, meetings, titles ...)

Ménard, SylvainUniversity of Quebec at Chicoutimi (author)
Hagberg, Delphine BardLund 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(Swepub:lu)bygg-dbr (author)
Kouyoumji, Jean LucFCBA Technical Institute (author)
Vardaxis, Nikolaos GeorgiosLund 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(Swepub:lu)cons-nkg (author)
University of Quebec at ChicoutimiTeknisk akustik (creator_code:org_t)

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In:Acoustics: MDPI AG4:1, s. 203-2262624-599X

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