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Record W4313568938 · doi:10.3390/civileng4010002

A Three-Phase Model to Evaluate Effects of Phase Diffusivity and Volume Fraction upon the Crack Propagation in Concrete Subjected to External Sulphate Attack

2023· article· en· W4313568938 on OpenAlexaff
Chaofan Yi, Zheng Chen, Jiamin Yu, Vivek Bindiganavile

Bibliographic record

VenueCivilEng · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsThermal diffusivityVolume fractionMortarMaterials scienceCrackingPermeability (electromagnetism)ExpansiveAggregate (composite)DiffusionPhase (matter)Penetration (warfare)Fraction (chemistry)Geotechnical engineeringComposite materialMechanicsGeologyChemistryThermodynamicsChromatographyEngineeringCompressive strength

Abstract

fetched live from OpenAlex

This study models concrete as a multi-phase system that comprises the mortar, coarse aggregates, and interfacial transition zones (ITZs). The diffusivity and the volumetric fraction of these phases are considered to propose a three-phase diffusion–reaction model to simulate the external sulphate attack. Furthermore, the parametric analysis alongside the sensitivity analysis is carried out to quantify the effect of these phases on the expansive cracking in concrete when exposed to a sulphate-rich environment. The results show that mortar dominates the sulphate ingress and the ensuing distress, while the ITZ is found to be least significant. Due to its significantly low permeability, the coarse aggregate may act as a “deceleration strip” or a “dam”, which in turn obstructs the sulphate penetration. More importantly, this effect is further noted to evolve with a decrease in the diffusivity and a rise in the volumetric fraction of coarse aggregates. As for ITZ, its volume fraction may play a more dominant role than its diffusivity on sulphate attack in concrete.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.498

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 head0.033
GPT teacher head0.328
Teacher spread0.294 · 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 routes1
Has abstractyes

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