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Record W4226218303 · doi:10.33593/a0mltw7m

Air Void System Requirements for Durable Paving Concrete - Another Look

2021· article· en· W4226218303 on OpenAlexaboutno aff
Seyedhamed Sadati, Xin Wang, Peter Taylor, John T. Kevern, Kejin Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsVoid (composites)DurabilityAir entrainmentMaterials scienceEnvironmental scienceComposite material

Abstract

fetched live from OpenAlex

The resistance of concrete to freeze-thaw (F-T) greatly depends on the characteristics of the concrete air-void system, such as the air content, size, and distribution. These air-void characteristics are influenced by every step of concrete production, from material selection and mixture proportioning to mixing, and placing. The research presented in this paper summarizes the observations from a comprehensive study that combined the in-situ performance of highway concrete with laboratory investigations. The field investigation involved determining the minimum requirements of air-void systems (AVS) for long-term durability against F-T cycles. In collaboration with several state agencies across the United States and Canada, core specimens were obtained for studying the properties of AVS resulting in different F-T conditions. Hardened air content, spacing factor, and specific surface of air-voids were determined for the extracted cores. The observations were supplemented by data obtained from a wide range of modern paving concrete mixtures. AVS characteristics were investigated using a variety of different techniques in fresh and hardened states. The main outcome of this research is a series of recommendations for minimum fresh and hardened AVS requirements to secure F-T durability.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.222
Teacher spread0.207 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same topicSmart Materials for ConstructionFrench-language works237,207