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Record W3165242308 · doi:10.11159/iccste21.130

Aggregate Moisture Content and Fresh Property Control Measures inCementitious Mortars

2021· article· en· W3165242308 on OpenAlexvenueno aff
Morgan C. Jenkins, Alexander S. Brand

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersOak Ridge National LaboratoryVirginia Polytechnic Institute and State University
KeywordsMortarAggregate (composite)CementitiousWater contentMoistureMaterials scienceComposite materialProperty (philosophy)Environmental sciencePulp and paper industryGeotechnical engineeringCementGeologyEngineering

Abstract

fetched live from OpenAlex

A primary objective for any concrete, or other cementitious composite, is to ensure consistent, reliable, and predictable fresh properties between subsequent batches. This is especially important for additive manufacturing, grouting, and pumping applications, where regulation of the fresh properties is paramount to providing quality control. This study considered the main influences of aggregate moisture content and how that moisture is accounted for during batching on the flow, setting time, and compressive strength of a mortar currently used in an additive manufacturing process. The results indicated that aggregate moisture content can drastically alter the variability in these properties. When fresh properties need to be controlled for a mortar, it is recommended that the aggregates should be saturated surface dry, or at least partially saturated, and that the moisture should be properly accounted for by adjusting the batching proportions. This recommendation is based on the results in this study with the lowest amount of variability and therefore the greatest amount of reliability and consistency.

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 score0.406
Threshold uncertainty score0.345

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 head0.023
GPT teacher head0.218
Teacher spread0.196 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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