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Record W2043951127 · doi:10.3141/2127-08

Low Design Temperatures of Asphalt Pavements in Dry–Freeze Regions

2009· article· en· W2043951127 on OpenAlexaboutno aff
Chun-Hsing Ho, Pedro Romero

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltFinite element methodPenetration (warfare)ThermalHeat transferPenetration testGeotechnical engineeringAsphalt pavementBoundary value problemPenetration depthThermal radiationEnvironmental scienceEngineeringStructural engineeringMaterials scienceMechanicsMeteorologyMathematicsComposite material

Abstract

fetched live from OpenAlex

This paper proposes three mathematical models that use solar radiation theory, transient heat transfer theory, and the finite element method to compute daily solar radiation, determine a thermal-penetration depth as a boundary condition, and eventually estimate pavement temperatures. The objective of this paper is to predict low design temperatures of asphalt pavements in dry–freeze regions. These step-by-step numerical analysis efforts provide pavement engineers and researchers with a method for prediction of low design temperatures of asphalt pavements. Daily solar radiation is calculated as input for determination of the thermal-penetration depth in a semi-infinite asphalt pavement system. Through use of the determined thermal-penetration depth as the prescribed temperature in the process of finite element analysis, the pavement temperature profile, including surface temperatures, can be better calculated. The finite element analysis results are verified with the SHRP, Canadian SHRP, and Superpave ® models and are validated with three sets of temperature data exported from the Long-Term Pavement Performance program in southern Utah. Comparison results present close agreement with the three predicted models and field temperatures with reasonable accuracy.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.341
Teacher spread0.282 · 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 designObservational
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
Published2009
Admission routes1
Has abstractyes

Explore more

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