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Record W2795459966 · doi:10.11159/icgre18.152

Sustainable Mixtures of TDA and Class A Gravel

2018· article· en· W2795459966 on OpenAlexafffundvenue
Mohammad Ashari, Hany El Naggar

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClass (philosophy)Environmental scienceComputer scienceBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Stockpiling or landfilling discarded tires poses major problems for the environment. In order to address these problems, several methods are currently employed to reuse discarded tires. One of these methods, which is gaining popularity, is to shred discarded tires into tire derived aggregates (TDA) and use them in civil engineering purposes such as backfill material for embankments or foundations. Despite the recent popularity of TDA, the experimental research to identify its geotechnical properties is limited or non-existent. Furthermore, most of the research done was either with small size TDA particles, to accommodate the readily available small-scale triaxial machines, or used direct shear apparatus which has limitations such as predefined failure surface and limited control over confinement pressure. In this study, the physical properties of five different TDA-gravel mixtures were evaluated using a large-scale triaxial machine. The TDA used in this study was the same size TDA used in civil engineering projects. All the tests were conducted according to ASTM standards. Finally, the results of deviatoric stress and volumetric strain vs axial strain for each mixture was reported and discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.655

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 head0.003
GPT teacher head0.167
Teacher spread0.165 · 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

Citations1
Published2018
Admission routes3
Has abstractyes

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