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Record W2015641484 · doi:10.1520/cca10531j

The Damage Rating Index Method for ASR Affected Concrete—A Critical Review of Petrographic Features of Deterioration and Evaluation Criteria

2002· article· en· W2015641484 on OpenAlexaffabout
Patrice Rivard, Benoît Fournier, Gérard Ballivy

Bibliographic record

VenueCement Concrete and Aggregates · 2002
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsNatural Resources CanadaUniversité de Sherbrooke
Fundersnot available
KeywordsPetrographyAggregate (composite)Alkali–silica reactionFeature (linguistics)Index (typography)GeologyMaterials scienceMineralogyComposite materialComputer science

Abstract

fetched live from OpenAlex

Abstract The Damage Rating Index method has recently been used with success in several cases of damage evaluation in structures affected by alkali-silica reaction in Canada and in Brazil. Although this petrographic method is starting to be widely used and is in the process of becoming integrated as a Canadian standard, it has not been modified yet from the original design. An evaluation of the method is presented in this paper. According to data obtained from many petrographic examinations, the number of cracks in coarse aggregates (filled or not with silica gel) seemed to show to best correlation with the expansion measured on laboratory concrete specimens made with Spratt limestone. The reaction rim is not a real “damage” feature and should not be considered as one but as a “degree of reaction” feature. In an attempt to improve the DRI method for assessing damage related to ASR, a new parameter should be introduced, which takes into account cracks running from aggregate particles to cement paste. The geological nature of the rock used as concrete aggregate may influence the reaction mechanism as well as the petrographic features related to ASR. Comparing concrete specimens subjected to ASR, which incorporate different aggregate types may, in some instances, be influenced by the type of reaction produced by the various reactive rocks and minerals in each aggregate.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.331
Teacher spread0.298 · 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

Citations43
Published2002
Admission routes2
Has abstractyes

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