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Record W3015690649 · doi:10.3390/ma13081804

Tomography-Based Investigation on the Carbonation Behavior through the Surface-Opening Cracks of Sliced Paste Specimen

2020· article· en· W3015690649 on OpenAlexaff
Dong Cui, Xiao-Bao Zuo, Keren Zheng, Sudip Talukdar

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsBritish Columbia Institute of Technology
FundersGovernment of Jiangsu Province
KeywordsCarbonationCrackingMaterials scienceComposite materialCarbonatation

Abstract

fetched live from OpenAlex

Understanding the cracking behavior during carbonation is of high importance, and the cracks can serve as a shortcut for CO2 diffusion, which can further accelerate the carbonation process itself. In this study, a sliced paste sample was taken for an accelerated carbonation test, and the cracking behavior, as well as its impact on carbonation, was investigated through a novel extended attenuation method based on X-ray (XRAM) which is performed primarily on computed tomography (CT). Surface-opening cracks at different carbonation ages were rendered, based on which a full view on the carbonation-cracking behavior was built. The results reveal that the crack paths can rapidly be occupied by CO2, and that leads to the generation of V-shaped carbonation cusps pervading the carbonation fronts. The V-shaped carbonation cusps were mostly generated at the early carbonation age (within 14 days), attesting to a less intact sample surface as compared to the inside area. Moreover, this study confirms that the carbonated area would split into two independent zones with variant carbonation degree due to the increased humidity level near the sample surface. The current work reveals the interconnection between carbonation and cracking, and the results can be used for the designing of cement-based materials with better carbonation and cracking resistance.

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 categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

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 head0.057
GPT teacher head0.251
Teacher spread0.194 · 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.

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
Published2020
Admission routes1
Has abstractyes

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