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Record W2054473503 · doi:10.1680/macr.2006.58.3.147

Autogenous deformations and viscoelasticity of UHPFRC in structures. Part II: numerical modelling

2006· article· en· W2054473503 on OpenAlexaff
Katrin Habel, Jean‐Philippe Charron, Emmanuel Denarié, Eugen Brühwiler

Bibliographic record

VenueMagazine of Concrete Research · 2006
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsPolytechnique Montréal
FundersNational Science Council
KeywordsShrinkageStructural engineeringParametric statisticsMaterials scienceViscoelasticityDeformation (meteorology)Composite numberReinforced concreteComposite materialEngineering

Abstract

fetched live from OpenAlex

Autogenous deformation is the main driving force for internal deformations in ultra-high performance fibre-reinforced concretes (UHPFRC). Experimental results of free and restrained shrinkage tests, described in part I of the current paper, are used to simulate the time-dependent behaviour of structural elements made of UHPFRC and normal-strength concrete (NSC). The validation of a numerical model is performed with experimental results of composite UHPFRC–concrete elements. The influence of the magnitude of UHPFRC autogenous shrinkage on deformations, stresses and crack formation in structural elements is determined in a parametric study. It is demonstrated that there are no excessive deformations and no crack formation in structural elements for magnitudes of autogenous shrinkage at 28 days below 1000 μm/m, that is, a value more than two times higher than that measured for the investigated UHPFRC.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.038
GPT teacher head0.282
Teacher spread0.244 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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