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Record W2047569983 · doi:10.1680/wama.13.00094

A bridge pier scour model with non-uniform sediments

2014· article· en· W2047569983 on OpenAlexafffund
Shaghayegh Pournazeri, S. Samuel Li, Fariborz Haghighat

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Water Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPierGeotechnical engineeringBridge scourGeologyChannel (broadcasting)Flow (mathematics)Sediment transportSedimentEngineeringStructural engineeringMechanicsGeomorphology

Abstract

fetched live from OpenAlex

Pier scour is a significant problem affecting the safety of bridges. For given hydraulic and geometric conditions, accurate prediction of scour with non-uniform sediments is important, but this need has not been fulfilled. The purpose of this research was to develop a three-dimensional model for scour prediction and to verify the model using laboratory measurements. The model allows for selective transport of non-uniform sediments, particle hiding and bed-level change in response to scour and deposition. The development of scouring around a circular pier on a mobile channel bed with non-uniform sediments was successfully predicted and scour depth prediction agreed well with the measurements. It was found that scour patterns emerge from the lateral sides of the pier and migrate towards its upstream nose. Upstream of the pier, strong downflow and vortex motions develop and effectively remove sediments from the foot of the pier; at equilibrium, the bed-surface slope almost reaches the angle of repose of sediments. On the upstream side, the scour hole has the shape of almost half a cone. Grain size non-uniformity reduces the magnitude of scour. These findings are of relevance to the safe and cost-effective design of pier foundations. The modelling techniques are computationally efficient and are useful for field-scale application.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.405

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations11
Published2014
Admission routes2
Has abstractyes

Explore more

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