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Record W2788200595 · doi:10.1520/jte20160667

Field Evaluation of Load-Bearing Capacity of Tire Fill Embankment Pavements

2018· article· en· W2788200595 on OpenAlexaffabout
Leila Hashemian, Alireza Bayat

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScrapLeveeGeotechnical engineeringBearing capacityLoad bearingEnvironmental scienceCivil engineeringEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Tire-derived aggregates (TDAs) are produced by shredding scrap tires. These materials have desirable engineering properties and can be appropriately used in pavement embankment fills. Several pavements have been constructed using TDA fill embankment; however, the seasonal performance of the pavements composed of these materials has not been widely investigated. This study investigates seasonal changes in load-bearing capacity of tire-filled embankment pavements after two years of construction in comparison to conventional pavement in a test road in Edmonton, Alberta, Canada. Three sections were constructed using different TDA materials, including passenger and light-truck tires (PLTT), off-the-road (OTR) tire particles, and a mix of PLTT and local subgrade soil, which was placed adjacent to a conventional section that acted as a control section. Falling weight deflectometer (FWD) tests were conducted in different seasons, and the back-calculation results revealed that although the subgrade of the TDA sections showed higher deflection and a lower resilient modulus compared with the control section, the load-bearing capacity of the TDA sections was greater than that for the control section. The section constructed using a mix of TDA material and soil showed almost the same performance as the control section.

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)0.003
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 score0.337
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 head0.078
GPT teacher head0.294
Teacher spread0.216 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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