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Record W3207806230 · doi:10.5539/mas.v15n6p1

Analysis of Abrasion Depth and Rates in Concrete

2021· article· en· W3207806230 on OpenAlexvenueno aff
Mohammed Saleh Al Ansari

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasion (mechanical)Silica fumeMaterials scienceComposite materialFiber-reinforced concretePolypropyleneErosionCementFiberGeology

Abstract

fetched live from OpenAlex

Abrasion is a major problem in hydraulic structures as they are continuously exposed to various types of water. Henceforth, these structures are susceptible to damage and require heavy maintenance. There is a significant demand in finding new techniques for improving the resistance towards the erosion of the concrete used in the construction of hydraulic structures. This work put forth a comparative analysis of the performance of two different types of concretes towards resistance namely, high-performance fiber reinforced concrete (HPFRC) [steel fibers (30 mm and 50 mm) and polypropylene fibers (19 mm)] and high-performance concrete (HPC). A comparative study was carried out based on their resistance towards wearing and hydro-abrasion erosion. The analyses were conducted using the WMP ECLIPSE method and ASTM C 1138 method. The results indicated that the rate of abrasion could be diminished by 18% based on the types of cement, fibers, concrete and modifications like the addition of silica fume.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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