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Record W7060940543

PERFORMANCE TESTING OF SELF-HEALING CAPSULES IN EARLY-AGE CEMENTITIOUS MATERIAL

2023· dissertation· en· W7060940543 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Technology SydneyUniversity of New South Wales
KeywordsCementitiousMixing (physics)RheologyHardening (computing)Classification of discontinuitiesVolume (thermodynamics)Fly ashMaterial properties
DOInot available

Abstract

fetched live from OpenAlex

Concrete cracks are inevitable due to chemical reactions and volume changes, as well as to environmental actions and mechanical loadings. Without proper repair, these cracks allow gases, liquids, and other deleterious materials to propagate into the concrete core. As a result, healing or sealing the cracks is pivotal to mitigate the occurrence of such deterioration. Encapsulation-based autonomous self-healing has been widely investigated as a solution; however, the efficacy of this technique is highly influenced by the performance of the capsules to protect the healing agents during concrete mixing and placing, while still triggering their release when young concrete cracks. Therefore, the objective of this study is to evaluate the performance of the self-healing capsules during concrete mixing and after hardening when the concrete undergoes volume changes due to hydration and drying. The initial stage of this research study was intended for reviewing the related literature to address the discontinuities and inconsistencies in the performance evaluation of self-healing cementitious materials. The following phases focused on testing the performance of self-healing capsules in early-age cementitious material employing fracture mechanics and finite elements techniques. The first phase focused on investigating the effectiveness of capsules in self-healing concrete at early-age providing insights into the design requirement for the success of the capsules. The second phase aimed at evaluating the performance of the self-healing capsules during concrete mixing. The correlations between the capsules’ shell properties, concrete rheological properties, capsules’ external forces, and capsule survival rate during concrete mixing were investigated. In the last phase, the performance of concrete containing self-healing capsules subjected to autogenous and drying shrinkage at an early-age was evaluated by numerically simulated ASTM C1581 restrained shrinkage test. The study accounted for the time-dependent concrete’s mechanical properties, and the capsule’s geometrical and mechanical properties. The results of this research provided in depth understanding of the performance evaluation of self-healing capsules in cementitious material during concrete mixing and after hardening. The developed models investigated the parameters that may contribute to the performance of the self-healing capsules, which can assist in manufacturing and designing of the capsules prior to their use in self-healing cementitious material applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.014
GPT teacher head0.219
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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