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Record W4400288272 · doi:10.1121/10.0026640

On the vibroacoustics of cross-laminated timber embedded with acoustic black holes

2024· article· en· W4400288272 on OpenAlexaff
Temitope Akinpelu, Joonhee Lee, Behrooz Yousefzadeh

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsConcordia University
Fundersnot available
KeywordsCross laminated timberAcousticsMaterials scienceComposite materialStructural engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The adoption of cross-laminated timber (CLT) in building construction is challenged by low-frequency impact noise. Conventional treatments typically involve increasing the mass of the floor assembly or employing structural decoupling techniques, both of which result in increased floor thickness. This study explores an alternative solution by examining the feasibility of embedding acoustic black holes (ABHs) into CLT floors. The ABH acts as a passive waveguide, concentrating vibrational energy in specific regions where it is transferred to damping layers and reduced. Using the finite element method, we investigated the vibroacoustic behavior of CLT plates within the 50–600 Hz frequency range. Our primary focus was on the influence of the ABH’s outer radius and the number of ABH inclusions within a CLT plate. The findings indicate that increasing the outer diameter of the ABH lowers the cut-on frequency and enhances the effective damping of the CLT plate. The increase in damping extends the effectiveness of the ABH to a broader frequency range, with the most noticeable effect above the cut-on frequency. The number of ABH inclusions impacts the structural modes below the cut-on frequency, and the optimal number of inclusions depends on the frequency of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.240
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicStructural Analysis of Composite MaterialsFrench-language works237,207