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Record W4410618523 · doi:10.1016/j.cement.2025.100146

Moisture dynamics and influence on alkali-silica reaction induced expansion: A comprehensive laboratory study

2025· article· en· W4410618523 on OpenAlexafffund
O. D. Olajide, Michelle Nokken, Leandro Sanchez

Bibliographic record

VenueCement · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of OttawaConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMoistureAlkali–silica reactionAlkali metalDynamics (music)Materials scienceChemistryComposite materialPsychologyOrganic chemistry

Abstract

fetched live from OpenAlex

Moisture availability is crucial for initiating and progressing alkali-silica reaction (ASR) in concrete. As a result, moisture control has often been adopted as a mitigation strategy in maintaining ASR-affected concrete. Selecting effective maintenance strategies requires a deep understanding of the moisture dynamics between internal moisture in concrete and its environment, and influence on ASR, which remains incompletely explored. To evaluate this interplay, 180 concrete cylinders incorporating a reference reactive aggregate (i.e., Spratt) were manufactured and stored at distinct conditions: i.e., three different temperatures (21°C, 38°C and 60°C) and five relative humidities (100% RH, 90% RH, 82% RH, 75% RH, and 62% RH). The internal and external relative humidity, length, and mass change were monitored for up to a year. Results indicate that the amount of water used for cement hydration is sufficient to trigger the reaction, regardless of the subsequent exposure condition. However, the rate of ASR-induced development is influenced by the internal relative humidity, which changes with time based on the external relative humidity and temperature. Additionally, the minimum moisture (i.e., RH threshold) required to cause significant deleterious effects from ASR was assessed and confirmed to be temperature-dependent.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.433

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)0.0000.000

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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