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Record W2773425052 · doi:10.1680/jmacr.17.00383

Structural implications of internal swelling reactions in concrete: review and research needs

2017· article· en· W2773425052 on OpenAlexaff
Martin Noël, Leandro Sanchez, Dana Tawil

Bibliographic record

VenueMagazine of Concrete Research · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEttringiteAggregate (composite)Parametric statisticsStructural materialScale (ratio)Risk analysis (engineering)SwellingAlkali–aggregate reactionComputer scienceForensic engineeringMaterials scienceEngineeringBusinessNanotechnologyPhysicsComposite materialMathematics

Abstract

fetched live from OpenAlex

The chemical and physical processes associated with alkali–aggregate reaction and delayed ettringite formation, otherwise known as internal swelling reactions (ISRs), have been the subject of considerable research spanning several decades, yet the implications for affected structures are still open to debate. A major knowledge gap currently exists between the diagnosis and prognosis of ISR mechanisms, and the quantification and management of risk associated with ISR expansion in structures. This lack of understanding of the effects of ISRs on structural behaviour may be attributed to the fact that parameters such as confinement, boundary conditions, anisotropy and loading conditions are complex and vary greatly in existing structures. Furthermore, the correlation between the results of microscopic/macroscopic analyses on concrete core samples and the in situ performance of large-scale structures is still not well understood, even at a fundamental level. The aim of this paper is to summarise current knowledge of the structural implications of ISR mechanisms in concrete structures and stimulate a discussion of the importance of pursuing multi-scale experimental testing programs for the assessment of ageing infrastructure. A parametric analysis is included using the software VecTor2.

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)0.005
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.402
Teacher spread0.307 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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