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Record W2110999335 · doi:10.1139/l02-054

NotePrediction of early age curling in thin concrete topping over wood floor systems

2002· article· en· W2110999335 on OpenAlexafffundvenue
Peter Lee, Ying Hei Chui, Ian Smith, Noel P. Mailvaganam, Gerry Pernica

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversity of New Brunswick
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsCurlingToppingShrinkageParametric statisticsFinite element methodStructural engineeringCreepGeotechnical engineeringMoistureMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

This paper presents finite element simulations of curling of unreinforced concrete topping laid over wood floor systems. The finite analysis consists of two parts. The first part calculates the relative moisture distribution with respect to the age of the concrete, while the second determines the topping curling deformation based on modulus of elasticity, density, and shrinkage of the concrete. With the finite element model the curling profile at any point in time can be predicted. Predictions agree reasonably well with measurements from a full-sized wood floor with a thin concrete topping. A model-based parametric study was performed. For the floor size investigated the results of the parametric study indicate that curling is greatly influenced by topping thickness and relative humidity of the surrounding air. Although the modelling as discussed is a preliminary approach, it provides a basis for further enhancements that will address factors such as creep and relaxation of concrete and deformation of the underlying floor system. Key words: finite element analysis, concrete topping, wood floor, curling, shrinkage.

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 score0.337
Threshold uncertainty score0.937

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 head0.012
GPT teacher head0.170
Teacher spread0.158 · 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
Published2002
Admission routes3
Has abstractyes

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