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Record W1986059315 · doi:10.1121/1.4806373

Effects of flooring, topping, and underlayment on impact sound insulation of wood-joisted floor-ceiling assemblies

2013· article· en· W1986059315 on OpenAlexaff
Lin Hu, Anes Omeranovic, Richard Dufour

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsFPInnovations
Fundersnot available
KeywordsCeiling (cloud)SoundproofingToppingSound transmission classStructural engineeringEngineeringMaterials scienceCivil engineeringComposite material

Abstract

fetched live from OpenAlex

Footstep impact noise transmission through floor-ceiling assemblies is a major source of complaints in wood-framed multi-family buildings. Experience shows that adding a finishing-topping-underlayment sandwich on a base floor-ceiling assembly significantly affects the noise transmission. So far, no reliable tool has been developed for designing the proper sandwich and most proposed solutions rely basically on trial and error. FPInnovations has launched a major research project to develop such tools. First phase of the project is focused on understanding the effects of flooring, topping and underlayment on the impact sound insulation. A mock-up assembly simulating a pair of typical stacked rooms was built. Standard field impact sound transmission tests were conducted on floors topped with a 1.2 m by 1.2 m four-layer sandwich patch. By varying the combination of flooring, topping and underlayment, over 50 patches were tested to better understand the effects of the type of materials in each layer on the assembly’s overall impact sound insulation. Based on a large number of tests conducted so far, it is evident that proper combination of flooring, topping and underlayment produces satisfactory impact sound insulation on wood-joisted floor-ceiling assembly. Verification testing on the assembly fully covered by the 4-layer samples is under way.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.352

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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