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Record W4415320017 · doi:10.1016/j.rineng.2025.107798

Self-healing capsules in early-age cementitious composites: A deterministic-probabilistic multi-scale approach

2025· article· en· W4415320017 on OpenAlexafffund
S.E. Chidiac, Shannon Guo

Bibliographic record

VenueResults in Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltimate tensile strengthHomogenization (climate)CreepCapsuleCementitiousProbability modelProbability distribution

Abstract

fetched live from OpenAlex

• Inherent variability affects capsule failure and effective properties of matrix. • Failure probability changes greatly with evolving early age concrete properties. • Reliability index for optimization of capsule design. • Neglecting drying creep effect overestimates probability of capsule rupture. Capsules for healing early-age concrete cracks should possess multi-functional properties to ensure shell rupture and release of healing agents rather than debonding from the cementitious matrix when intercepting a crack. A deterministic-probabilistic multi-scale model is developed to determine the probability of capsule rupture versus interfacial debonding induced by early age volume change. A micromechanical model along with a probabilistic model are employed for determining the maximum tensile stresses in capsule shell, radial tensile stresses on the capsule-matrix interface, and the corresponding probability index of capsule rupture and debonding. The matrix is modeled as an equivalent homogeneous medium with effective properties determined from a homogenization scheme. The influence of crack is accounted for implicitly. The model results are qualitatively in agreement with results reported in the literature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.676

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 designSimulation or modeling
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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