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Record W2023282287 · doi:10.1139/t09-036

Evaluating shear mobilization in rockfill columns used for riverbank stabilization

2009· article· en· W2023282287 on OpenAlexaffvenueabout
Marolo Alfaro, James Blatz, Wisam F. Abdulrazaq, Chang‐Seok Kim

Bibliographic record

VenueCanadian Geotechnical Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsShearing (physics)Geotechnical engineeringSiltGeologyDirect shear testShear (geology)Shear stressMaterials scienceGeomorphology

Abstract

fetched live from OpenAlex

The major rivers within the City of Winnipeg are founded in glacial Lake Agassiz clay and silt sediments that have low shear strength. As such, riverbank instabilities are a common issue along many stretches of the rivers. The use of rockfill columns has become an increasingly utilized approach for stabilizing failing banks. Recent cases in Winnipeg have shown that movements can occur following installation of rockfill columns. Uncertainty regarding the magnitude of these movements that is required to mobilize shearing resistance in the rockfill columns has resulted in situations where the stability of riverbanks following remediation has been questioned. This has provided a need to improve our understanding about how much movement a stabilized slope must undergo before sufficient shear resistance of the rockfill column will be mobilized. The results of experimental testing conducted to assess the shear mobilization of rockfill column materials using a large-scale direct shear test apparatus are presented in this paper. The testing methodology is described along with the test results. The results show that the development of material specifications, construction, and placement methods and appropriate methods of analysis requires understanding of the stress–strain properties of the in situ soil and the rockfill material.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.691
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations8
Published2009
Admission routes3
Has abstractyes

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