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Record W4380449397 · doi:10.52202/069179-0247

DESIGN OF LONG-SPAN LIGHTWEIGHT TIMBER FLOORS SUBJECT TO WALKING EXCITATIONS: A CASE STUDY

2023· article· en· W4380449397 on OpenAlexaff
Hassan Karampour, Farid Piran, Adam Faircloth, Chandan Kumar, David Zhang, Benoit P. Gilbert, Hong Guan, Lin Hu, Ying Hei Chui, Wen‐Shao Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of AlbertaFPInnovations
Fundersnot available
KeywordsTrussFlangeDeflection (physics)VibrationStructural engineeringLimitingComputer scienceVibration controlEngineeringArchitectural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Lightweight timber construction is popular in buildings with two or more storeys in Australia. The floors are made of a floorboard supported on joists or trusses. Recently, the sector is moving towards multi-storey construction of different building classes with different floor usages and shared tenancy. Thus, a need for high performing lightweight floor systems becomes urgent. The current vibration control criteria in the Australian standard recommends limiting static deflection, which is a coarse method and does not necessarily guarantee satisfactory performance. In the current study, vibration performance of a 6m6m floor system with particleboard flange and truss webs is investigated under single walker excitations and at different walking frequencies. The vibration responses are compared to the predictions and performance criteria recommended in international standards and guidelines. The results show inconsistencies in the calculated levels of acceptance from different sources using simplified expressions and more rigorous methods of analysis. This highlights the significance of the need for further research to develop a harmonised method of analysis that can be used by manufacturers and engineers in Australia to confidently design floors for vibrations.

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 score0.460
Threshold uncertainty score0.456

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 communication0.0000.000
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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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
Published2023
Admission routes1
Has abstractyes

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