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Record W4225160405 · doi:10.11159/icsect22.132

Shear Strength of Steel Fiber-Reinforced Recycled Aggregates Concrete Deep Beams

2022· article· en· W4225160405 on OpenAlex
Nancy Kachouh, Tamer El‐Maaddawy, Hilal El-Hassan, Bilal El-Ariss

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceShear (geology)Composite materialCrackingStiffnessVolume fractionBeam (structure)Structural engineeringShear strength (soil)Geology

Abstract

fetched live from OpenAlex

The effectiveness of steel fibers to improve the shear behavior of recycled aggregates concrete deep beams is investigated experimentally in this paper. Three large-scale concrete deep beams with a shear span-to-depth ratio (a/h) of 1.6 were tested. Two beams had a 100% recycled concrete aggregates (RCA) replacement ratio whereas one beam was made with natural aggregates (NA) to act as a benchmark. One of the RCA beams included steel fibers at a volume fraction of 1%. The use of a 100% RCA significantly impaired the beam stiffness, reduced the cracking load by 25%, and decreased the shear strength by 5%. The use of steel fibers in a RCA beam delayed initiation of shear cracks, improved the beam stiffness, and fully restored the original shear capacity.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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