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Record W2969455817 · doi:10.1139/cjce-2019-0303

Evaluation of recycled asphalt mixture at low temperature using different analytical solutions

2019· article· en· W2969455817 on OpenAlexvenueno aff
Augusto Cannone Falchetto, Ki Hoon Moon, Dong Hyuk Kim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceJames Cook University
KeywordsCreepAsphaltRheometerCrackingMaterials scienceStiffnessBendingStress (linguistics)Asphalt pavementViscoelasticityModulusThermalRutStructural engineeringComposite materialGeotechnical engineeringRheologyEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Using reclaimed asphalt pavement (RAP) in road infrastructures is crucial for mitigating the environmental impact while controlling the construction costs. However, poorer low temperature performance may be experienced for mixtures containing RAP. In this paper, the effect of RAP on the material response at low temperature is investigated through mixture creep testing with the bending beam rheometer. Three different mathematical approaches are selected for further evaluation in combination with simple statistical analysis. Based on the experimental data, creep stiffness, m-value, relaxation modulus, thermal stress, and critical cracking temperature are computed and compared. As a result, no differences are found between the virgin mixture and that designed with 15% of RAP. Poorer performance is observed when more than 25% of RAP is incorporated; however, no significant variation was observed for a further increase up to 40% suggesting that higher amount RAP could be used depending on traffic level and climate.

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 score0.035
Threshold uncertainty score0.906

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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