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Record W2749049077 · doi:10.2166/ws.2017.168

An analytical solution for steady seepage into a defective pipe

2017· article· en· W2749049077 on OpenAlexaff
Yao Tang, Dave Chan, David Z. Zhu, Shuai Guo

Bibliographic record

VenueWater Science & Technology Water Supply · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParametric statisticsBody orificeHydraulic headInfiltration (HVAC)GroundwaterMechanicsPressure headVolumetric flow rateFinite element methodPipe flowGroundwater flowGeotechnical engineeringWater flowPipe network analysisMaterials scienceGeologyMathematicsEngineeringStructural engineeringAquiferPhysicsComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract An analytical solution was proposed for the groundwater flow through a defective pipe, which can be used to estimate the water flow rate into the pipe and predict the pore water pressure distribution in surrounding soils. This analytical solution was verified by comparing with experimental results, and the predicted pressure distribution around the defective pipe is proved to be consistent with numerical simulations using the finite element method. From the parametric analysis, the infiltration rate increases as the defect position changes from top to bottom on the pipe, and the effect of defect position is not significant if the water head above the defect is 10 times greater than pipe radius. An approximated solution for estimating the groundwater flow infiltration rate through a circular orifice on the pipe is proposed as well. From the verification and parametric studies, this proposed analytical solution is proved to be an efficient approach for the estimation of groundwater infiltration through a defective pipe.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.246
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2017
Admission routes1
Has abstractyes

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