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Record W3216249062 · doi:10.1016/j.jmrt.2021.11.121

Tensile behavior of crack-repaired ultra-high-performance fiber-reinforced concrete under corrosive environment

2021· article· en· W3216249062 on OpenAlexaff
Doo‐Yeol Yoo, Taekgeun Oh, Wonsik Shin, Soonho Kim, Nemkumar Banthia

Bibliographic record

VenueJournal of Materials Research and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of Korea
KeywordsMaterials scienceUltimate tensile strengthCorrosionComposite materialEpoxyCrackingFiber-reinforced concreteFiber

Abstract

fetched live from OpenAlex

This study aims to evaluate the influence of crack repair using epoxy sealing on the tensile response of ultra-high-performance fiber-reinforced concrete (UHPFRC) under corrosive environments. Three different crack widths, i.e., 0.1, 0.3, and 0.5 mm, and two different corrosion durations, i.e., 4 and 10 weeks, were considered. The test results indicated a minor change in the tensile performance of UHPFRC with the smallest crack width of 0.1 mm under corrosive environments for up to 10 weeks. This is due to the restriction of ferric oxide formation at the densified fiber–matrix interface. A wider crack width accelerated the steel fiber corrosion and noticeably influenced the post-cracking tensile behavior. Considering the corrosion duration of 4 weeks, the tensile strength of cracked UHPFRC with a width of 0.3 mm or greater increased by approximately 12–17% owing to the moderate steel fiber corrosion. However, the tensile strength decreased during the longer corrosion duration of 10 weeks by ruptures of excessively corroded steel fibers. Crack repair using epoxy sealing increased the tensile strength of cracked UHPFRC by approximately 10% and effectively prevented further corrosion of steel fibers at the crack location, leading to higher tensile strength even after 10 weeks of corrosion.

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 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 score0.001
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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