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Record W4415881248 · doi:10.1617/s11527-025-02809-4

Test methods for chloride diffusivity of blended cement pastes: a review by RILEM TC 298-EBD

2025· article· en· W4415881248 on OpenAlexaff
Neven Ukrainczyk, T. Bernard, Arezou Babaahmadi, Liming Huang, Christoph Zausinger, Anthony Soive, Stéphanie Bonnet, Fabien Georget, Maruša Mrak, Matej Dolenec, Tobias Völker, Prannoy Suraneni, William Wilson

Bibliographic record

VenueMaterials and Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
FundersTechnische Universität Darmstadt
KeywordsCementitiousThermal diffusivityDurabilityChlorideAggregate (composite)Solid mechanicsCement

Abstract

fetched live from OpenAlex

Abstract The use of supplementary cementitious materials (SCM) is an important part of the roadmap for reducing CO 2 emissions and extending the service life of reinforced concrete structures. To accelerate the adoption of SCMs, the RILEM Technical Committee 298-EBD evaluates scaled-down cement paste test methods to assess the effect of SCM on resistance to chloride and sulfate ingress and reactivity, which are critical to concrete durability. This review focuses on methods for measuring chloride diffusivity and is divided into four sections: diffusivity models and parameters, diffusion test methods (including NMR and chloride measurements), migration test methods and implications for future research. Key insights highlight the complexities of multi-species ionic and molecular diffusion/migration, including various binding interactions, and compares the different measurement methodologies. The review also addresses the test scale and aggregate effects, noting the pros and cons of testing at the paste, mortar, and concrete scales. The review underscores the need for further investigation into testing protocols and the influence of SCM on chloride diffusion, emphasizing that comprehensive testing across different scales provides complementary information for assessing durability performance.

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)0.000
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 score0.031
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.324
Teacher spread0.311 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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