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Vibration Performance of Lightweight Cold-Formed Steel Floors

2009· article· en· W2168167056 on OpenAlexaff
Russell Parnell, B. Davis, Lei Xu

Bibliographic record

VenueJournal of Structural Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of WaterlooDillon ConsultingUniversity of Calgary
Fundersnot available
KeywordsStructural engineeringServiceability (structure)Deflection (physics)DeckFraming (construction)VibrationJoistToppingEngineeringDamping ratioNatural frequencyModalMaterials scienceComposite materialAcoustics

Abstract

fetched live from OpenAlex

A study investigating the modal properties and dynamic response of several laboratory-constructed and in situ floors supported with cold-formed steel C-shaped joists for floors was conducted. The tested floors were typical of residential midrise applications, with oriented-strand board, FORTACRETE, and cold-formed steel deck subfloors, both with and without lightweight concrete topping. Details including span, large lip-reinforced web openings, subfloor, topping, strongback, and framing condition were varied to observe their influence on the fundamental frequency, damping ratio, and deflection. Suggestions for the design and remediation of floors where vibration serviceability is a concern are given. Laboratory tested floor systems were generally found to be the worst-case scenario for the natural frequency and damping ratio. Furniture and finishes were found to not appreciably change the performance of a floor system. The responses of the floor systems tested in this study were evaluated against the ISO 2631 limit for maximum acceleration and Onysko’s static deflection limit, as presented in ATC Design Guide 1: Minimizing Floor Vibration. The in situ floors examined were found to have performed within the acceptable range, as defined by the two criteria.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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 designObservational
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

Citations37
Published2009
Admission routes1
Has abstractyes

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