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Record W4292148147 · doi:10.1139/cjce-2022-0172

Comparison of moisture susceptibility of Evotherm 3G warm mix asphalt versus hot mix asphalt

2022· article· en· W4292148147 on OpenAlexafffundvenue
Xiomara Sánchez, Sina Varamini

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersMinistère des Transports
KeywordsRutAsphaltMoistureAggregate (composite)Asphalt pavementComposite materialMaterials scienceUltimate tensile strengthGeotechnical engineeringEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Asphalt stripping is a common form of distress caused by moisture that creates a loss of bond between the aggregate and binder. It is thought that the use of warm mix asphalt (WMA) technology could lead to increased moisture susceptibility if the free moisture present in the aggregates does not evaporate at the lower mixing temperatures. The purpose of this study is to evaluate the moisture susceptibility of WMA using Evotherm 3G on laboratory-prepared and plant-prepared mixes. The moisture damage potential was evaluated using the TSR (tensile strength ratio), the static immersion test, moisture-induced stress tester, and Hamburg wheel tracking test. Additionally, plant-prepared mixes were also assessed for TSR and rut depth. The results indicated that WMA provided adequate levels of resistance to moisture damage, reduced swelling, and improved coating. Laboratory mixes showed reduced resistance to rutting, but rut depth of plant mixes was similar to that of hot mix asphalt.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.258
Teacher spread0.233 · 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.

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

Citations3
Published2022
Admission routes3
Has abstractyes

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