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Record W2036036135 · doi:10.3141/1989-42

Comprehensive Field Studies to Address the Performance of Stabilized Expansive Clays

2007· article· en· W2036036135 on OpenAlexaff
Anand J. Puppala, Gautham S. Pillappa, Laureano R. Hoyos, Deepti Vasudev, Deepti Devulapalli

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2007
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsLimeEttringiteGeotechnical engineeringExpansive claySoil stabilizationCementCalcium oxideSubgradeSubsoilCalcium silicate hydrateFly ashSulfateEnvironmental scienceGeologyPortland cementSoil waterMaterials scienceComposite materialMetallurgySoil science

Abstract

fetched live from OpenAlex

This work was conducted as part of a research study for the City of Arlington, Texas, to explore and develop alternative stabilization methods for sulfate-rich soils located in southern parts of the city. As a result of a literature review of sulfate-rich expansive soil treatments and comprehensive laboratory studies, four stabilizers were recommended for field treatment studies: sulfate-resistant cement (Type V), low-calcium Class F fly ash with Type V cement, ground granulated blast furnace slag, and lime mixed with polypropylene fibers. The four stabilizers, along with control lime treatment, were used to modify subsoil near Harwood Road in South Arlington. Rigid pavements were then constructed on the stabilizers' sections, and these sections were instrumented with strain gauges and pressure cells. Pavement instrumentation was then monitored to address load transfer mechanisms and stabilized materials' compression behavior under traffic loads. Elevation surveys were also conducted to evaluate swell movements of the treated subsoils. X-ray diffraction analyses were conducted on treated subgrade specimens to address the formation of ettringite mineral. On the basis of these evaluations, the performance of the four stabilization methods was compared with that of the control lime 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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.111
GPT teacher head0.395
Teacher spread0.284 · 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 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

Citations9
Published2007
Admission routes1
Has abstractyes

Explore more

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