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Record W2100746152 · doi:10.1139/l08-049

Mechanical performance and crack retardation study of a fiberglass-grid-reinforced asphalt concrete system

2008· article· en· W2100746152 on OpenAlexaffvenue
S. Joon Lee

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsSaint-Gobain (Canada)
Fundersnot available
KeywordsMaterials scienceCrackingAsphalt concreteOverlayAsphaltComposite materialViscoelasticityFlexural strengthStructural engineeringBendingGridThree point flexural testUltimate tensile strengthEngineeringComputer scienceGeology

Abstract

fetched live from OpenAlex

This paper presents constitutive design considerations for a semi-rigid, resin-impregnated fiberglass grid that enhances the mechanical performance of an asphalt concrete overlay and provides a pattern to retard crack propagation. An acrylic polymer resin covers the grid-structured fiberglass strands, thus making their viscoelastic nature compatible with the asphalt materials. The remarkable temperature susceptibility of the resin is observed using time–temperature superposition. The grid tensile strength of 100 kN/m was qualified using standard measurements. Three-point bending and cyclic fatigue loading mechanical tests affirm the retardation performance of the grid-reinforced system in terms of crack propagation. The retardation mechanism is affected by synchronizing the cracking pattern with the flexural stress–strain profile through image analysis. A National Center for Asphalt Technology full-scale road testing program provides assessment of the grid-reinforced pavements. A visual inspection of the grid extracted from the traffick zone showed fair condition of the pavement.

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)0.000
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 score0.018
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.191
Teacher spread0.178 · 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

Citations28
Published2008
Admission routes2
Has abstractyes

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