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Record W4379380690 · doi:10.21838/uhpc.16689

Evaluation of CSA Prequalification Procedures of UHPC Materials for Bridge Construction

2023· article· en· W4379380690 on OpenAlexaffabout
Zoi G. Ralli, Syed Mohd. Ashraf Husain, S. J. Pantazopoulou, Emad Booya, Philip Loh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of WindsorYork University
Fundersnot available
KeywordsDurabilityEngineeringRetrofittingForensic engineeringConstruction engineeringStructural engineeringComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Canadian Bridges are particularly vulnerable to corrosion and long-term durability problems initiated by the easy fracture and delamination of concrete under combined stress and climatic exposure. UHPC is an alternative construction material that holds great promise to alleviate many of those durability and strength problems both in new construction and in retrofitting. While only recently it was considered an emerging material, UHPC is now implemented in infrastructure, necessitating full understanding of material behavior with ultimate goal to exploit its unique properties in design practices. In this study, a proprietary UHPC mix produced by DURA Canada is used to assess the material characterization techniques prescribed by Canadian Standards Association (CSA) for UHPC materials. This research serves as a case study for proof testing the repeatability and robustness of the prequalification procedures specified by the2019 CSA Standards, in light of the fact that these procedures have only been recently drafted and introduced in the Code. Additional objective is to evaluate the material’s compliance with requirements for abrasion, salt scaling, absorption, chloride ion penetration, and freeze thaw resistance according to the different standards used by the Canadian Industry as well as time dependent properties such as creep, shrinkage, and coefficient of thermal expansion. Finally, this paper will present in detail the specimen preparation and testing, as well as the challenges encountered, lessons learnt and recommendations for future editions of the code.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.354
Teacher spread0.261 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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Same topicConcrete and Cement Materials ResearchFrench-language works237,207