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Record W4388191574 · doi:10.18280/mmep.100524

Clear Water Scour at Varied Pile-Cap Elevation and Skewed Bridge Piers

2023· article· en· W4388191574 on OpenAlexvenueno aff
Noor S. Hussein, Abdul H. Shukur, Zaid Hameed Majeed

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPileElevation (ballistics)Pile capGeologyGeotechnical engineeringPierBridge scourBridge (graph theory)Hydrology (agriculture)EngineeringStructural engineeringBiology

Abstract

fetched live from OpenAlex

The effect of pile-cap elevation and skew angles on the local scour brought on by clearwater flow around complex bridge piers was investigated experimentally.The complex Selebat and suspension bridge piers in the Al-Muthana and Al-Basra Governorates, respectively, were replicated using two river models scaled at a ratio of one to fifty.Three different pile-cap altitudes and four different pier skew angles (0, 30, 45, and 60) were varied in the experiment.The results show that the equilibrium depth of scour (ds) is greatly increased by even a little skew angle added to previously aligned piers.Due to its wide-pier characteristic, the column contribution to the scour depth becomes predominate when a pier is located asymmetrically in relation to the flow.Three forms of pier skewness were distinguished: aligned piers (=0°), slightly skewed piers (=30°), and severely skewed piers (45°).The influence of pile-cap and pile group on the scouring action decreased as the skewness increased.Furthermore, equilibrium scour holes often reveal pier components uniformly, independent of the undisturbed bed level, particularly strongly skewed piers with entirely buried pile-caps.This study provides insightful information on the roles of pile-cap height and skew angles in flow-induced scouring, which may help designers and maintenance professionals develop more efficient design and preventative measures.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score0.937

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 head0.019
GPT teacher head0.190
Teacher spread0.171 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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