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Record W4405640728 · doi:10.14796/jwmm.s534

Numerical Modeling of Water Flow in Permeable Friction Course Pavement During Rainfall Considering Rainfall Intensity and Suction Pressure

2024· article· en· W4405640728 on OpenAlexvenueno aff
Phuong Khanh Chau, Anh Tuấn Lê, Thai Tran, Tu-Quyen Thi Tran, Tan Hung Nguyen

Bibliographic record

VenueJournal of Water Management Modeling · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
FundersHo Chi Minh City University of Technology and Education
KeywordsSuctionCourse (navigation)Intensity (physics)Geotechnical engineeringEnvironmental scienceFlow (mathematics)Hydrology (agriculture)GeologyMechanicsMeteorologyEngineeringGeographyPhysics

Abstract

fetched live from OpenAlex

This study developed a numerical model to observe the water flow in permeable friction course pavement (PFCP) during rainfall events. The model was established based on FeniCS, which is widely known as an open-source computing platform for solving partial differential equations. The results showed that rainfall intensity significantly affected time for surface ponding in the PFCP. A higher rainfall intensity resulted in a lower time for surface ponding of the PFCP. The evaluation for the effect of suction pressure on the PFCP showed that the suction pressure in the PFCP had a remarkable effect on the time for surface ponding. As the suction pressure in the PFCP increased, the time for surface ponding increased. The results in this study are based on numerical modeling. In the future, further experimental studies in the laboratory and in the field are needed to validate the water flow in PFCP during rainfall events considering other factors such as permeability, rutting, and environmental factors.

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 score0.143
Threshold uncertainty score0.585

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 head0.009
GPT teacher head0.203
Teacher spread0.193 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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