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A Contribution to Understanding Damage Generated and Propagated by the AAR Reactive Aggregate from Kingston, Ontario, Canada

2023· preprint· en· W4388931025 on OpenAlexafffundabout
Cassandra Trottier, Leandro Sanchez

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsExpansiveAggregate (composite)Alkali–aggregate reactionDistressAlkali–silica reactionMaterials scienceForensic engineeringComposite materialEngineeringPsychologyClinical psychology

Abstract

fetched live from OpenAlex

It remains unclear in the literature what is the cause of the so-called alkali-carbonate reaction (ACR) damage to concrete yet, a consensus is reached when considering dedolimitization as non-expansive. However, expansion and cracks as distress features are often attributed to alkali-silica reaction (ASR) when evidence of a reactive mineral is found in the aggregate. Evidently, different types of aggregates may produce different types and degrees of damage. Therefore, this work aims to assess the damage to concrete generated and propagated by the so-called ACR susceptible reactive Kingston coarse aggregate through mechanical testing (i.e., direct shear test), microscopy (the damage rating index – DRI), and other techniques. Distinct induced expansion levels (i.e., 0%, 0.05%, 0.12%, and 0.20%) were selected to compare the distress caused by ACR to concrete affected by ASR. Result show that the behaviour of ACR, namely as captured through the DRI, is inconsistent with that of ASR thus attesting to ACR being a distinct distress mechanism.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.101
GPT teacher head0.291
Teacher spread0.190 · 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 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

Citations1
Published2023
Admission routes3
Has abstractyes

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Same venuePreprints.orgSame topicConcrete and Cement Materials ResearchFrench-language works237,207