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Record W1971350603 · doi:10.1139/t01-032

Fracture toughness of frozen base and subbase soils in pavement

2001· article· en· W1971350603 on OpenAlexvenueno aff
Jean‐Marie Konrad, Julie Cummings

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsFracture toughnessGeotechnical engineeringMaterials scienceSubbaseComposite materialFracture mechanicsToughnessFracture (geology)Crushed stoneWater contentGeology

Abstract

fetched live from OpenAlex

Temperature distribution in the pavement structure, moisture distribution in granular soils, modulus of the asphalt concrete, and fracture toughness of material in the pavement structure strongly influence the propagation and spacing of thermal contraction cracks. Fracture toughness was determined for frozen sand (subbase layer) and frozen crushed stone (base layer) by adapting established fracture mechanics test procedures recommended in American Society for Testing and Materials standard test method E399-83 for metals. It was established that fracture toughness increases with decreasing temperature and increasing volumetric ice content. For a temperature of –5°C, the fracture toughness of frozen crushed stone increased almost linearly from 0.05 to 0.40 MPa·m 0.5 when the volumetric ice content increased from 6 to 14%. For frozen sand, the fracture toughness K IC in a wedge-opening mode increased from 0.04 to 0.70 MPa·m 0.5 when the volumetric ice content increased from 8 to 28%. It was also established that the fracture toughness of frozen soil decreases with decreasing soil average grain size according to a logarithmic law.Key words: fracture, toughness, experimental, frozen, granular soil, 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.998

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)0.0030.000

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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 designObservational
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

Citations22
Published2001
Admission routes1
Has abstractyes

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