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Record W4220880985 · doi:10.1061/9780784484036.029

Assessing Piping Risks by Finite Elements

2022· article· en· W4220880985 on OpenAlex
Bryant A. Robbins, D. V. Griffiths, Gordon A. Fenton

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

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPipingRandom variableLeveeErosionFinite element methodFoundation (evidence)Geotechnical engineeringEvent (particle physics)GeologyComputer scienceEngineeringStatisticsMathematicsStructural engineeringEnvironmental engineeringGeomorphologyGeography

Abstract

fetched live from OpenAlex

Backward erosion piping (BEP) is a leading cause of failure in dams and levees, but uncertainties in erosion progression characteristics and soil properties provide significant challenges to a deterministic analysis approach. For this reason, a risk-based approach to BEP failure is of great interest to engineers. The probability of failure is most commonly determined through the use of event trees and engineering judgment. When using this approach, geologic variability in the subsurface is incorporated into risk analysis through estimates of subjective probabilities regarding the existence of a continuous, erodible layer in the foundation. Recently, however, there have been developments in the random finite element method (RFEM) modeling of BEP progression that have demonstrated alternate means of assessing the probability of pipe progression in spatially variable soils. In this study, an RFEM model for BEP progression is presented. Results are presented to illustrate the influence of soil variability on the probability of BEP progression. Results indicate that the probability of BEP progression increases as the spatial correlation length increases.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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