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Record W4367053399 · doi:10.3390/ma16093374

The Impact of Distinct Superplasticizers on the Degradation of Concrete Affected by Alkali-Silica Reaction (ASR)

2023· article· en· W4367053399 on OpenAlexafffund
Andisheh Zahedi, Cassandra Trottier, Yufeng Zhu, Leandro Sanchez

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
FundersUniversity of OttawaUniversité Laval
KeywordsAlkali–silica reactionSuperplasticizerMaterials scienceAlkali metalDegradation (telecommunications)Composite materialAlkali–aggregate reactionCompressive strengthChemistryCementOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

, respectively) on the development of an alkali-silica reaction (ASR) was investigated through the use of multilevel assessment. This testing protocol showed promising results for evaluating concrete damage due to ASRs based on mechanical and microscopical testing protocols, specifically the stiffness damage test (SDT) and the damage rating index (DRI). Concrete specimens that incorporated the aforementioned SPs and distinct reactive aggregates (coarse and fine) were manufactured and then stored in conditions that enabled ASR development and were monitored over time. Upon reaching the desired expansion levels of this study, the concrete specimens were prepared for the multilevel assessment. The results show that the SP-incorporated concrete specimens with lower and higher alkali content yielded lower and higher deterioration results, respectively. This clearly confirms that while SP-incorporated concrete that contains SPs with a higher alkali content could increase the risk of ASR deterioration, those SPs with a very low amount of alkali content could act as a mitigation strategy against ASRs. Finally, an investigation into the influence of distinct SPs on the chemical composition of an ASR gel was conducted, which confirmed that the SP with a higher alkali content had the highest potential for further deterioration.

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)0.000
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 score0.008
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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