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Evaluating the efficiency of SCMs to avoid or mitigate ASR-induced expansion and deterioration through a multi-level assessment

2023· article· en· W4384276876 on OpenAlexafffund
Diego Jesus De Souza, Leandro Sanchez

Bibliographic record

VenueCement and Concrete Research · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaEuropean CommissionUniversity of Ottawa
KeywordsAlkali–silica reactionMaterials scienceAlkali–aggregate reactionAggregate (composite)CementComposite materialEnvironmental science

Abstract

fetched live from OpenAlex

It is widely accepted that Alkali-Silica Reaction (ASR)-induced expansion and deterioration may be prevented by the appropriate use of supplementary cementing materials (SCMs). Nevertheless, the correlation between microscopic/macroscopic damage degree and features of SCMs-made concrete is still not fully understood. Moreover, more research needs to be conducted to understand the impact of SCMs on the elastic properties of ASR-secondary products. This research presents the results of a multi-level assessment of ASR-induced damage development in concrete incorporating a wide range of reactive aggregates and SCMs at selected unrestrained expansion levels (i.e., 0.00 %, 0.05 %, 0.12 %, 0.20 % and 0.30 %). Furthermore, a thorough discussion is made on the damage generation and propagation at distinct locations of affected concrete: aggregate particles, ITZ and cement paste. Results indicate that higher volumes of SCMs can slightly change ASR damage development by lowering the formation of cracks in the cement paste and altering ASR reaction products' chemo-mechanical properties.

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)0.003
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 score0.389
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.413
GPT teacher head0.492
Teacher spread0.080 · 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

Citations32
Published2023
Admission routes2
Has abstractyes

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