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Record W2588183374 · doi:10.1139/cjce-2016-0458

Investigation of thermal-induced strains in flexible pavements based on field data

2017· article· en· W2588183374 on OpenAlexaffvenueabout
Mohammad Hossein Shafiee, Simita Biswas, Negar Tavaf Zadeh, Leila Hashemian, Alireza Bayat

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
FundersInternational Retinal Research Foundation
KeywordsAsphalt pavementAsphaltThermalThermal expansionEnvironmental scienceSpring (device)AnisotropyTransverse planeGeotechnical engineeringMaterials scienceStructural engineeringGeologyMeteorologyComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Thermal-induced strains caused by daily temperature fluctuations are considered to be a direct impact of environmental factors on flexible pavements. This paper investigates thermal-induced strains in the longitudinal, transverse, and vertical directions at the bottom of hot mix asphalt (HMA) during a 16 month monitoring period. This study was conducted at the Integrated Road Research Facility (IRRF), which is a fully instrumented test road in Edmonton, Alberta, Canada. Noticeable variations for horizontal and vertical strains were observed as a function of ambient air temperature change. Results showed that the highest strains occurred in winter and spring, while the most pronounced strain fluctuations were captured during the spring-thaw period. Using the obtained data, coefficients of thermal contraction and expansion were determined during different seasons. It was found that thermal coefficients are different in three directions, illustrating the anisotropic properties of HMA.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.066
GPT teacher head0.263
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations1
Published2017
Admission routes3
Has abstractyes

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