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Record W2158399286 · doi:10.1002/stco.201110014

Design of floor structures against human‐induced vibrations

2011· article· en· W2158399286 on OpenAlexaff
Stephen Hicks, Andrew Smith

Bibliographic record

VenueSteel Construction · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsRed Deer Polytechnic
Fundersnot available
KeywordsScope (computer science)ModalEngineeringSimple (philosophy)VibrationStructural engineeringPedestrianCivil engineeringComputer scienceArchitectural engineeringAcousticsMaterials science

Abstract

fetched live from OpenAlex

Abstract A simple method for designing floor structures against humaninduced vibrations is presented in a joint JRC‐ECCS publication, which was developed from two major European research projects supported by the Research Fund for Coal and Steel (RFCS). The simple method is appropriate for floors where the response is dominated by the first eigenmode being excited by walking. This paper presents a general design method that was also developed within the scope of these RFCS projects and is based on a modal superposition approach. The method has a wider range of application as it may be applied to any floor type and, in addition to walking, other human activities can be readily included. The general design methodology has been successfully used in the UK since 2004 on a variety of steel‐framed floors, which have included office, hospital, residential and dance floors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.224
Teacher spread0.187 · 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 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

Citations12
Published2011
Admission routes1
Has abstractyes

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