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Effect of freeze-thaw cycling on fatigue behaviour in concrete

2019· article· en· W2982432702 on OpenAlexaff
Andrew J. Boyd, Andrea Leone

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsUltimate tensile strengthMaterials scienceCrackingComposite materialCyclingTemperature cyclingTension (geology)Thermal

Abstract

fetched live from OpenAlex

Abstract Freeze-thaw damage is a deterioration mechanism caused when saturated concrete is exposed to temperature variations that cycle above and below its freezing point. The expansion of water in the concrete pore space due to freezing can lead to internal pressures that induce cracking, thus expediting the deterioration process through freeze-thaw cycles. The purpose of this investigation was is to evaluate the effect of freeze-thaw deterioration on the tensile fatigue life of air-entrained concrete at early stages of deterioration. Concrete cylinders were prepared with 0.45 and 0.65 water to cement ratios (W/C), then cured for 28 days before being subjected to the freeze-thaw cycling. After 0, 25 and 50 freeze-thaw cycles, the pressure tension (PT) test was used to induce tensile fatigue loading cycles until failure. The PT was capable of determining the decrease in fatigue life, represented as the number of cycles to failure, of the concretes when subjected to pure cyclical tensile loading after having first been exposed to freezing and thawing cycles. The results indicated that even though the ultimate static tensile strength of the specimens did not vary significantly due to the freeze-thaw cycles, the residual fatigue properties were degraded. UPV monitoring was able to determine the increase in internal damage, as the UPV of the specimens decreased with continued freeze-thaw cycling. It was shown that freeze-thaw deterioration reduces the ability of concrete to withstand cyclic loading, even at early stages prior to a decrease in static tensile strength.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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