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Record W3116022177 · doi:10.22215/etd/2015-10914

Air-Borne Sound Transmission through Triple-Leaf Walls: Modelling, Measurement and Optimization

2015· dissertation· en· W3116022177 on OpenAlexaff
Armin Eslami

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsSound transmission classSoundproofingPerforationAcousticsStatistical energy analysisShear wallTransmission (telecommunications)Resistive touchscreenTransfer matrixCavity wallStructural engineeringMaterials scienceEngineeringFinite element methodPhysicsComputer scienceMechanical engineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

The Midply™ triple-leaf shear resistive wall is designed by FPInnovations and UBC to be employed in mid-rise wood building.Compared to double-leaf structures, this wall has a weaker low-frequency sound insulation due to the additional resonance created by the middle-leaf.The original contribution of this thesis is developing a method to predict the air-borne sound transmission through triple-leaf walls, which can incorporate perforated plates.The model is based on a modified Transfer Matrix Method (TMM) that accounts for the losses at the perimeter of the finite cavity.The air-borne sound transmission tests performed on simplified small-scale structures showed that the modified TMM model has acceptable predictions in most frequencies, although Statistical Energy Analysis (SEA) was superior for high-frequency predictions.The research suggests that the sound insulation in triple-leaf structures could be improved through careful perforation of the middleleaf, which is suggested for future work.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.284
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2015
Admission routes1
Has abstractyes

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