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Record W4388518060 · doi:10.1139/cjce-2023-0243

Engineering properties-based parameters used for moisture damage evaluation of asphalt mixtures: a review

2023· review· en· W4388518060 on OpenAlexvenueno aff
Thanh Chung

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typereview
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAsphaltMoistureCohesion (chemistry)Ultimate tensile strengthGeotechnical engineeringAsphalt pavementMaterials scienceComposite materialModulusDynamic modulusAsphalt concreteEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Moisture-induced damage has been known as a major premature distresses in asphalt pavement. Numerous review papers have been made to provide the progression in study on moisture damage of asphalt pavement. However, mechanical properties of asphalt mixtures used as a parameter for moisture susceptibility evaluation were not reviewed in detail. To make the review of moisture-induced damage in asphalt pavement more comprehensive, this paper reviews research studies on moisture damage of asphalt pavement in view of mechanical properties as a parameter during the past 50 years. These parameters include tensile strength ratio (TSR), cohesion ratio (CR), dynamic modulus ratio (DMR), resilient modulus ratio (RMR), and shear strength ratio (SSR). TSR and CR are only relevant at bottom of asphalt concrete (AC), while SSR can be used to describe moisture damage at different AC depths. DMR and RMR are concentrated on the impact of moisture damage via performance characteristics of asphalt mixtures.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.303
Teacher spread0.212 · 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
GenreReview

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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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