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Record W2341566168 · doi:10.3390/ma9040294

Ultrasonic Monitoring of Setting and Strength Development of Ultra-High-Performance Concrete

2016· article· en· W2341566168 on OpenAlexaff
Doo‐Yeol Yoo, Hyun-Oh Shin, Young‐Soo Yoon

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthComposite materialShrinkagePenetration (warfare)Curing (chemistry)Ultrasonic sensorPenetration depthExpansiveCompressive strengthEngineering

Abstract

fetched live from OpenAlex

In this study, the setting and tensile strength development of ultra-high-performance concrete (UHPC) at a very early age was investigated by performing the penetration resistance test (ASTM C403), as well as the direct tensile test using the newly developed test apparatus, and taking ultrasonic pulse velocity (UPV) measurements. In order to determine the optimum surface treatment method for preventing rapid surface drying of UHPC, four different methods were examined: plastic sheet, curing cover, membrane-forming compound, and paraffin oil. Based on the test results, the use of paraffin oil was found to be the best choice for measuring the penetration resistance and the UPV, and attaching the plastic sheet to the exposed surface was considered to be a simple method for preventing the rapid surface drying of UHPC elements. An S-shaped tensile strength development at a very early age (before 24 h) was experimentally obtained, and it was predicted by a power function of UPV. Lastly, the addition of shrinkage-reducing and expansive admixtures resulted in more rapid development of penetration resistance and UPV of UHPC.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.210
Teacher spread0.201 · 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 teacher head, 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

Citations29
Published2016
Admission routes1
Has abstractyes

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