MétaCan
Menu
Back to cohort
Record W2150105788 · doi:10.1177/0021998311399481

Effect of surface treatment on the post-peak residual strength and toughness of polypropylene/polyethylene-blended fiber-reinforced concrete

2011· article· en· W2150105788 on OpenAlexafffund
Pouria Payrow, Michelle Nokken, D. Banu, D. Feldman

Bibliographic record

VenueJournal of Composite Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsMaterials scienceComposite materialPolypropyleneFlexural strengthCompressive strengthToughnessContact angleFiberPolyethyleneWettingSurface modificationSynthetic fiber

Abstract

fetched live from OpenAlex

This study involved an experimental investigation into the improvement of mechanical properties of fiber-reinforced concrete (FRC) utilizing chemically treated polypropylene/polyethylene fibers. Four types of chemical surface treatments were examined: two types of chromic acid (Types B and C), potassium permanganate (PP), and hydrogen peroxide solutions. Untreated and treated fibers were added at 0.32% by volume of concrete and also at 0.50% for the best treatment technique. Compressive and flexural strength were measured to quantify improvement. It was found that there were no significant differences in compressive strength. Type B chromic acid solution was found to be the most effective technique in improving the flexural strength of FRC resulting in average increases of 8.9% and 17.6% for peak and residual strengths, respectively, compared to nontreated fibers. While not as effective as plasma treatments, further research may be warranted for chemical treatments. Surface wettability of the treated fibers was measured by contact angle with water. The contact angle was found to have no correlation to the toughness. The higher volume of fibers, both treated and nontreated gave higher residual strength and toughness; however, surface treatment did not significantly enhance mechanical properties.

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 score0.004
Threshold uncertainty score0.879

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 head0.013
GPT teacher head0.231
Teacher spread0.217 · 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

Citations30
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Composite MaterialsSame topicInnovative concrete reinforcement materialsFrench-language works237,207