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Record W1878207610

ABRASION RESISTANCE OF CONCRETE – DESIGN, CONSTRUCTION AND CASE STUDY

2015· article· en· W1878207610 on OpenAlexaboutno aff
Md. Safiuddin, Benjamin Scott

Bibliographic record

VenueConcrete research letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasion (mechanical)CrackingCementitiousMaterials scienceCuring (chemistry)CorrosionCementComposite materialForensic engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

One of the most common forms of deterioration imposed on concrete structures is surface abrasion. This mechanical wearing can be a catalyst for other forms of deterioration such as cracking and corrosion of reinforcing steel. This report is intended to discuss three aspects of abrasion. Firstly, the common sources and mechanics of the abrasion of concrete have been identified. Secondly, a literature review has been presented identifying the material aspects and construction methods that impact the abrasion resistance of concrete, including compressive strength, water to cementitious materials ratio, type and size of aggregates, supplementary cementitious materials, chemical admixtures, and curing and finishing practices. Recommendations for specifying and designing concrete mixes, as well as suggested finishing practices that improve abrasion resistance are provided. Finally, a case study has been presented on the ice shield design of Confederation Bridge in Canada, the longest bridge in the world over ice covered water, showcasing how engineers handled the abrasion of concrete in their designs.

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)0.001
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.078
GPT teacher head0.321
Teacher spread0.243 · 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 designCase report
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

Citations70
Published2015
Admission routes1
Has abstractyes

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