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Assessment of Potential for Seepage-Induced Unraveling Failure of Flow-Through Rockfill Dams

2012· article· en· W2008703502 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Geomechanics · 2012
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFlow (mathematics)Geotechnical engineeringGeologyParametric statisticsNonlinear systemBoundary value problemDarcy's lawMechanicsMathematicsMathematical analysisPhysicsPorosity

Abstract

fetched live from OpenAlex

A purely numerical parametric study of 24 flow-through rockfill dam geometries was conducted. The nonlinear nature of the p-LaPlacian–like partial differential equation was dealt with using a finite-difference scheme that directly incorporated the exponent of a power law that replaced Darcy’s law. Convergence, use of specialty nodes, nodal density, and boundary condition effects were quantitatively investigated. The flow-field angle of the toe was found to be a useful starting point in studying the potential for unraveling failure. Factors of safety (FS) against this type of failure are then presented for a range of downstream slopes, thus showing which combinations of slope and particle diameter are unsafe. It is shown that the FS tends to drop below unity under the seepage face primarily because of the strength of the exit gradient near the toe of the structure and secondarily because of the overflow velocity. It is hoped that the techniques and results presented will facilitate the design and assessment of flow-through rockfill structures.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.479

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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