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Record W2991222690 · doi:10.1139/cjce-2019-0480

Rheological, mechanical, and abrasion characteristics of polymer-modified cement mortar and concrete

2019· article· en· W2991222690 on OpenAlexaffvenue
Changjun Zhou, Liangliang Chen, Shaopeng Zheng, Yunxi Xu, Decheng Feng

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsImpact
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsMaterials scienceComposite materialCementMortarAbrasion (mechanical)RheologyFlexural strengthCompressive strengthNatural rubber

Abstract

fetched live from OpenAlex

The settlement of fresh concrete or improper operation during construction usually makes the water to cement (w/c) ratio of the cement mortar on the concrete pavement surface higher than designed, which may influence the abrasion resistance of concrete pavement. This study firstly tried to establish relationships between abrasion of hardened cement mortar and rheological characteristics of fresh cement mortar in the laboratory. Secondly, a polymer-modified cement mortar was developed with carboxyl styrene butadiene rubber latex added to the cement mortar. Its rheological properties in mixing, mechanical properties, and abrasion were investigated. It is found that the polymer-modified cement mortar has a much better abrasion resistance and flexural strength while lower compressive strength than cement mortar. A stronger correlation was observed between flexural strength rather than compressive strength and abrasion resistance of cement mortar. The proposed polymer-modified concrete also exhibited good abrasion resistance.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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