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Record W7125389057 · doi:10.5281/zenodo.18335115

Experimental Investigations on Plastic Reinforced Concrete

2023· article· W7125389057 on OpenAlexaff
Jasiya J, Jerin Joseph, John Tony V, Sanju Sajeev, Sreeja M D

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Language
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsDispose patternCompressive strengthUltimate tensile strengthDurabilityCementPlastic wasteReuse

Abstract

fetched live from OpenAlex

Concrete is most widely used material for the construction of commercial and industrial infrastructure. Despite its high compressive strength, it has very low tensile strength, which is why it is reinforced with steel bar. However, steel reinforcement corrodes and expands over time, which impairs their functionality and affecting the durability of the concrete. Plastic waste is one of the challenges to dispose and manage as it is non-biodegradable material which is harmful to our environment. Our work mainly focuses on the reuse of plastic waste and thus helps to reduce the presence of plastic wastes in environment. This project mainly focuses on the evaluation of flexural strength, split tensile strength, compressive strength by the addition of plastic strips in the form of mesh in 3 layers. This work also includes a comparative study on the strength of plastic reinforced concrete and plain cement concrete.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.257
Teacher spread0.208 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicInnovative concrete reinforcement materialsFrench-language works237,207