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Record W4416095379 · doi:10.3397/in_2025_1076858

Investigating flanking noise transmission in mass timber construction through laboratory measurements of isolated floor toppings

2025· article· en· W4416095379 on OpenAlexaboutno aff
Jim Best, Aedan Callaghan

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsFlanking maneuverTransmission (telecommunications)Noise (video)VibrationNoise controlSound transmission classPower transmissionAttenuation

Abstract

fetched live from OpenAlex

Mass timber is quickly becoming a popular design choice for multifamily residential development, with recent changes to building code allowing high-rise mass timber and the push towards lower embodied carbon or net zero construction. Cross Laminated Timber floor panel layouts and structural grids often result in continuous panels spanning between units or require rigid connections between panels, thus flanking noise transmission between units is an especially important acoustical design consideration. Quantifying the flanking noise transmission performance of a junction in an acoustical assembly requires knowledge of the vibration attenuation characteristics of its elements. The vibration reduction index (Kij) measures the transmission loss of a junctions' flanking paths by quantifying the transmission of vibrational power through its structural elements. This study utilizes Canada's National Research Council's four-room flanking facility to measure the Kij of flanking paths through a CLT wall/floor junction with and without isolated floor toppings on a resilient interlayer. Apparent Sound Transmission Class (ASTC) is then calculated from the measured Kij. The analysis presented investigates flanking noise transmission of a floating floor on a Cross Laminated Timber Panel spanning between the four rooms, with additional research into the effect of adding bulkheads on the ceiling side of the junction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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