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A study of the effect of coconut fiber on reinforced concrete beams subjected to combined bending and shear

2024· article· en· W4403035489 on OpenAlex
Samina. M. Kazi, Ashok Ramkrishna Munde, Vijay Shivaji Shingade, Sonal Vaibhav Shelar, Vaibhav Vilas Shelar

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Journal of Advanced Engineering Technology and Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsTrinity College
Fundersnot available
KeywordsMaterials scienceShear (geology)Composite materialStructural engineeringBendingReinforced concreteFiberEngineering

Abstract

fetched live from OpenAlex

This review paper examines the impact of coconut fiber reinforcement on the behavior of reinforced concrete beams subjected to combined bending and shear. Coconut fibers, also known as coir fibers, are a sustainable and cost-effective alternative to traditional steel reinforcement. The use of coconut fibers in concrete has shown promise in enhancing the ductility, energy absorption capacity, fracture resistance, and durability of concrete structures. The study highlights the mechanical properties and applications of coconut fiber reinforced concrete (CFRC) and investigates the effects of varying fiber content on concrete performance. Factors affecting the properties of fiber-reinforced concrete, such as the relative fiber matrix index, volume of fibers, aspect ratio of fibers, fiber orientation, workability, compaction, size of coarse aggregate, and mixing techniques, are also discussed. Understanding these factors is crucial for designing and constructing durable and sustainable concrete structures.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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