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Effect of Curing Age on Pull-Out Response of Carbon, Steel, and Synthetic Fiber Embedded in Cementitious Mortar Matrix

2022· article· en· W4288488711 on OpenAlexaff
Maryam Monazami, Rishi Gupta

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials scienceComposite materialPolyacrylonitrileCuring (chemistry)Ultimate tensile strengthBond strengthFiberMortarCementSynthetic fiberPolymerAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Fiber-matrix interface bonding is an important area of concern in fiber-reinforced composites because it is directly related to the mechanical behavior of composites. In this study, interfacial bond properties of four types of fiber at different curing ages were investigated by analyzing fiber pull-out responses. Special attention was given to the fiber–matrix bonding behavior, including fiber tensile strength, average bond strength, equivalent bond strength, and average pull-out energy. Pull-out tests were conducted at 7, 14, 28, and 56 days of curing. Different failure modes in steel fiber, synthetic macrofiber, polyacrylonitrile (PAN)-based carbon fiber, and pitch-based carbon fiber were investigated. According to the pull-out force versus slip curves, different failure patterns were recorded based on the fiber type. Carbon fibers experienced a sudden drop after reaching the peak load, whereas the load decrease in steel and synthetic fiber was not as abrupt. Results also confirmed that steel fibers exhibited the highest pull-out load and energy absorption capacity followed by lower values for synthetic and carbon fibers. While monofilament of steel fiber was able to absorb 1,050 N·mm, monofilament of synthetic fiber and twisted bundles of carbon fibers could absorb 277 and 55 N·mm, respectively. However, the bond strength of straight carbon fibers was comparable to that of synthetic fiber and still lower than steel fiber. It was also derived from the experimental data that an increase in cement matrix age correlates to an improvement in fiber maximum pull-out load, bond strength, and tensile strength. These parameters were identified and compared in all fiber types.

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)0.002
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 score0.102
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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