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Record W4226105236 · doi:10.22215/etd/2022-14876

Development and Application of Simplified Methods for Analysis of Floors under Human-induced Vibrations

2022· dissertation· en· W4226105236 on OpenAlexaff
Rahul Saini

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsVibrationModular designAccelerationRange (aeronautics)Structural engineeringParametric statisticsEngineeringForcing (mathematics)Transformation (genetics)Parametric modelComputer scienceNatural frequencyAcousticsMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

This study presents a rational simplified model for vibration assessment of floors under walking load. The model is developed by converting the floor system into an equivalent SDOF model using appropriate transformation factors and applying simplified forcing functions to simulate the walking load. The accuracy of the proposed model is verified against experimental tests and detailed FE analyses. A parametric study is also carried out on 67 floors with a wide range of design parameters. The results show that, unlike most existing simplified vibration analysis methods which have a limited application range, the proposed model is able to accurately capture the peak acceleration of floors with different frequencies and structural systems. The model is then used for the vibration design of floors of modular hospitals. It is demonstrated that the proposed SDOF model greatly facilitates the design of modular floors with high natural frequencies, which is almost impractical to do with existing simplified vibration analysis methods.

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: none
Teacher disagreement score0.749
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.030
GPT teacher head0.363
Teacher spread0.333 · 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
Published2022
Admission routes1
Has abstractyes

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