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Record W4388796012 · doi:10.3390/ma16227221

Investigation of Rutting Performance in Geogrid-Reinforced Asphalt by Penetration Test

2023· article· en· W4388796012 on OpenAlexaff
Shenglin Wang, Danrong Wang, Susan Tighe, Sam Bhat, Shunde Yin

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsGeogridRutAsphaltPenetration (warfare)Geotechnical engineeringMaterials scienceComposite materialPenetration testAsphalt concreteReinforcementEngineering

Abstract

fetched live from OpenAlex

Permanent deformation, or rutting, is one of several critical distresses in flexible pavements. This paper introduced a novel experimental method, a penetration test, for asphalt mixtures to quantify the effects of glass fibre geogrids embedded in asphalt under repeated loading. It was found that the evolution of permanent deformation (εp) and its strain rate have three clearly identifiable stages. It was also observed that the presence of the geogrid increased the flow number and the number of cycles to failure significantly compared to control samples. Some of the current εp fitting models were found to be valid for deformation prediction under penetration. In addition, a new simple FN calculation method was also proposed based on strain rate and it showed consistent results. In particular, geogrid type “Grid10”, which has smaller aperture size (12.7 mm) had slightly better reinforcement performance regarding the rutting resistance due to its larger contact area. Overall, the test and data analysis method presented in this study could be an important reference for future investigations on geosynthetic-reinforced pavement materials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.458

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)0.0000.000

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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 designBench or experimental
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

Citations2
Published2023
Admission routes1
Has abstractyes

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