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Record W4412723633 · doi:10.14796/jwmm.c557

Study of Flow Around Developed Permeable Pile Groyne

2025· article· en· W4412723633 on OpenAlexvenueno aff
Andi Patiroi, Adi Hendra, Tia Hetwisari, Sekaring Bumi Ingerawi

Bibliographic record

VenueJournal of Water Management Modeling · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPileGeotechnical engineeringGeologyFlow (mathematics)MechanicsPhysics

Abstract

fetched live from OpenAlex

Scouring and erosion commonly occur to form river morphology. This process could become a disaster if not managed properly. One of the solutions to this problem is controlling river flow and bed changes using groyne construction. Researchers have been studying the structure of groynes for decades, specifically focusing on the permeable pile groyne. In this study, the stability of the structure was considered to develop four models of permeable pile groynes, which were then compared with pile groyne models based on the Indonesia National Standard. Flow patterns and surface water elevation were mathematically investigated. The results show that the deceleration of velocity downstream of pile groynes can be controlled by adjusting the pile spacing. Three types of pile spacing in a group of groynes, (i.e., parallel to the flow, perpendicular to the flow, and oblique to the flow), significantly affect the flow pattern. Permeable pile groynes adhering to the Indonesian standard demonstrated reduction in velocity up to 83.68%. Meanwhile, the developed model M5 achieved velocity reductions of 72.64%. Model M5 is anticipated to be more stable due to higher permeability and the number of pile groups against with the lateral force. Consequently, this model could serve an alternative to the Indonesia National Standard for groyne design.

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)0.000
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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.241
Teacher spread0.221 · 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
Published2025
Admission routes1
Has abstractyes

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