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Record W2993336164

Complexities of Curtain Wall Flanking Transmission – A Case Study

2019· article· en· W2993336164 on OpenAlexvenueno aff
Kelly Kruger, Robert Ogle

Bibliographic record

VenueCanadian acoustics · 2019
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsnot available
Fundersnot available
KeywordsFlanking maneuverCurtain wallFacadeTransmission (telecommunications)Sound transmission classEngineeringPath (computing)Structural engineeringComputer scienceTelecommunicationsCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

Curtain wall construction is very common in modern commercial buildings. The continuous external facade can lead to significant limitations on the sound isolation between adjacent rooms, separated either laterally or vertically. Flanking transmission of curtain wall systems is very difficult to predict at the design stage of a project. Manufactures do not routinely measure this parameter. The scarcity of data is partly due to a lack of laboratories that have the necessary specialized test environment. Generic flanking transmission loss data is of little value because of the large variation in curtain wall assemblies. Designers are often left with best guess approximations based on previous experience. These factors  also  make it difficult to significantly improve flanking transmission of an existing curtain wall installation. This paper describes a project where significant flanking along the curtain wall resulted in poor sound isolation between floors. Due to the complexity of the junction between the curtain wall and the floor structure, several potential flanking paths were identified and evaluated. It was possible to alter each flanking path individually so that the incremental improvement of each step could be quantified. After implementing several modifications, an improvement of approximately 15 dB was observed across a wide frequency range.

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 score0.132
Threshold uncertainty score0.971

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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
Published2019
Admission routes1
Has abstractyes

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