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Record W3153907200 · doi:10.11159/ijci.2021.010

Workability and Early-Age Strength of Recycled Aggregate Concrete Incorporating Basalt Fibers

2021· article· en· W3153907200 on OpenAlexvenueno aff
Shahrukh Shoaib, Hilal El-Hassan, Bilal El-Ariss, Tamer El‐Maaddawy

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2021
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAggregate (composite)BasaltMaterials scienceComposite materialBasalt fiberFiberGeologyGeochemistry

Abstract

fetched live from OpenAlex

This research investigates the effect of basalt fibers (BF) on the workability and early-age compressive and splitting tensile strengths of concrete made with 100% recycled concrete aggregates (RCA). The target concrete compressive strengths were 30, 45, and 60 MPa, whereas the basalt fibers had a length of either 20 or 43 mm. The addition of BF significantly decreased the workability, slightly improved the compressive strength, and remarkably increased the splitting tensile strength of the RCAbased concrete. The compressive strengths of the RCA-based concrete with different BF lengths and volume fractions were insignificantly different. The original compressive strength of the natural aggregate (NA)-based concrete was not fully restored, irrespective of the BF length and volume fraction. Basalt fibers had a more pronounced effect on improving the splitting tensile strength rather than the compressive strength. The original splitting tensile strength of the NA-based concrete was fully restored in most of the cases. Basalt fibers with a length of 43 mm were more effective in improving the splitting tensile strength of the RCA-based concrete than those having a length of 20 mm.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.217
Teacher spread0.211 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Civil InfrastructureSame topicRecycled Aggregate Concrete PerformanceFrench-language works237,207