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Record W2316385939 · doi:10.1520/acem20150027

Performance of Fiber-Reinforced Concrete Made With Basalt-Bundled Fibers

2016· article· en· W2316385939 on OpenAlexaff
P. Bhama Iyer, Sara Kenno, Sreekanta Das

Bibliographic record

VenueAdvances in Civil Engineering Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsWindsor Clinical ResearchUniversity of Windsor
Fundersnot available
KeywordsBasalt fiberMaterials scienceComposite materialFiberBasaltGeology

Abstract

fetched live from OpenAlex

Abstract Fiber-reinforced concrete (FRC) has become a viable new material used in various flatwork construction projects, such as building pavements, large industrial floors, and runways. In this research, basalt chopped fibers in bundled form were used to make an FRC material called basalt fiber-reinforced concrete (BFRC) to study the possible improvement in the 28-day compressive strength and modulus of rupture, although the latter one is the more important factor in flatwork construction. The basalt fiber specimens were cast using basalt-bundled fibers of three different lengths and three different amounts. The results indicated that 36-mm-long chopped basalt-bundled fibers, and the fiber amount of 8 kg/m3 are the optimum fiber length and fiber quantity for achieving the best performance of both the compressive strength and modulus of rupture. This paper discusses the test matrix and test results obtained from various BFRC and plain concrete specimens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2016
Admission routes1
Has abstractyes

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