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Record W421756114 · doi:10.14264/346023

The nature of reactive powder concrete affecting its viability as a large scale construction material

2002· dissertation· en· W421756114 on OpenAlexaboutno aff
Michael Barnes

Bibliographic record

VenueThe University of Queensland · 2002
Typedissertation
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamEngineeringScale (ratio)Bridge (graph theory)Forensic engineeringConstruction engineeringCivil engineeringGeographyCartographyPolitical science

Abstract

fetched live from OpenAlex

Over the last twenty years, high performance concretes have gained recognition as important large scale construction materials. These ‘traditional’ high performance concretes (HPC’s) have been used in mainstream construction with Characteristic strengths ranging between 50 and 120 MPa (Bonneau et al 1997). Although very similar to normal strength concretes, various admixtures and water reducing agents set HPC’s apart and give them their unique characteristics. Reactive Powder Concretes follow a totally different approach in the way they are prepared and used in structural applications. The contents of this report will occasionally compare these differences.Reactive Powder Concrete was first developed in the early 1990's by researchers from the French construction company Bouygues S.A., Paris, France. Marcel Cheyrezy is the man credited as the leading force in the developmental phase of RPC. Cheyrezy is the director of research and development of the Scientific Division at Bouygues. Pierre Claude Aitcin, Scientific Director of Concrete Canada at the University of Sherbrooke, was instrumental in moving RPC from the lab to the field with the application of RPC on the Sherbrooke Pedestrian/Bikeway Bridge in Sherbrooke, Quebec, Canada. This first example of RPC in construction is being closely followed by other experimental structures all over the world.....

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.638

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 head0.005
GPT teacher head0.200
Teacher spread0.196 · 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 designBench or experimental
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 routes1
Has abstractyes

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