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Record W4206961694 · doi:10.32920/16850119

Experimental investigation on repair and self-healing of tension-induced cracks in reinforced concrete panels

2021· preprint· en· W4206961694 on OpenAlexaff
Alireza Ahmadi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceComposite materialEpoxyUltimate tensile strengthFly ashBrittlenessCrackingHydrostatic pressureTension (geology)Self-healingLeakage (economics)Fibre-reinforced plastic

Abstract

fetched live from OpenAlex

<div>This research concentrates on repair and self-healing of tension cracks in RC panels subjected to direct tensile loading and hydrostatic water pressure by incorporating high-performance materials including High-strength epoxy, Glass Fiber Reinforced Polymer (GFRP) laminate, Engineered Cementitious Composite (ECC) slag, and ECC fly ash. The test setup simulates repairing internal surface of a cracked wall segment of a containment exposed to internal hydrostatic pressure. The study showed the applicability of ECC fly ash in repairing RC specimens with the ability to restore the structural strength along with reduced cracking, leakage, and enhanced self-healing behavior. GFRP exhibited high effectiveness in waterproofing and repair of damaged RC structures.</div><div>Applicability of epoxy injection in local repair of cracked RC specimens was confirmed, while brittle failure of epoxy resulted in development of a large crack and high leakage rate. Moreover, GFRP was found to be the most economical technique for repair of RC containments.</div>

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 score0.029
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 science0.0000.001
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 head0.029
GPT teacher head0.261
Teacher spread0.233 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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