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Record W1972125366 · doi:10.3141/2441-09

Methodology for Identifying Zero-Stress Time for Jointed Plain Concrete Pavements

2014· article· en· W1972125366 on OpenAlexaff
Somayeh Nassiri, Julie M. Vandenbossche, Donald J Janssen

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2014
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsUniversity of Alberta
FundersPennsylvania Department of Transportation
KeywordsSlabGeotechnical engineeringGeologyCementStructural engineeringStress (linguistics)Temperature gradientMaterials scienceEngineeringComposite materialPhysicsMeteorology

Abstract

fetched live from OpenAlex

This study focused on identifying the zero-stress time (TZ) in jointed plain concrete pavements (JPCPs). TZ is the time when the concrete slab is sufficiently strong to deform (thermally expand or contract and thus curl) despite the existing external restraints, including the friction at the base-slab interface. It is critical to be able to identify TZ so that the temperature gradient present in the slab at TZ, known as the built-in temperature gradient, can be characterized. In this study, TZ was established through the instrumentation of 36 concrete slabs in four JPCP construction projects. Strain-temperature behavior in each slab was used to identify TZ. The slabs in each project were paved at different times of the day (morning, noon, early afternoon, and late afternoon) to investigate the effects of the ambient curing conditions on TZ. The degree of hydration at TZ (α TZ ) was established for each slab. The field data were used in the development of a model for predicting α TZ as a function of the concrete water-to-cement ratio, unit weight, early-age elastic modulus, and slab thickness.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.208
GPT teacher head0.407
Teacher spread0.199 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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