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Record W2786354892 · doi:10.1155/2018/2134873

Mechanical Properties and Durability of Latex‐Modified Fiber‐Reinforced Concrete: A Tunnel Liner Application

2018· article· en· W2786354892 on OpenAlexaff
Joo-Ha Lee, Hwang-Hee Kim, Sung‐Ki Park, Ri-On Oh, Hae‐Do Kim, Chan-Gi Park

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsContech (Canada)
Fundersnot available
KeywordsMaterials scienceComposite materialDurabilityFiber-reinforced concreteFlexural strengthCompressive strengthFiberGround granulated blast-furnace slagCement

Abstract

fetched live from OpenAlex

This study assessed the mechanical properties and durability of latex‐modified fiber‐reinforced segment concrete (polyolefin‐based macrosynthetic fibers and hybrid fiber‐macrosynthetic fiber and polypropylene fiber) for a tunnel liner application. The tested macrosynthetic fiber‐reinforced concrete has a better strength than steel fiber‐reinforced concrete. The tested concrete with blast furnace slag has a higher chloride ion penetration resistance (less permeable), but its compressive and flexural strengths can be reduced with blast furnace slag content increase. Also, the hybrid fiber‐reinforced concrete has higher compressive strength, flexural strength, chloride ion water permeability resistance, impact resistance, and abrasion resistance than the macrosynthetic fiber‐reinforced concrete. The modified fiber improved the performance of concrete, and the hybrid fiber was found to control the formation of micro‐ and macrocracks more effectively. Therefore, overall performance of the hybrid fiber‐reinforced concrete was found superior to the other fiber‐reinforced concrete mixes tested for this study. The test results also indicated that macrosynthetic fiber could replace the steel fiber as a concrete reinforcement.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.544

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 communication0.0000.001
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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