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Record W2768846030 · doi:10.1139/cgj-2017-0238

Clogging of pervious concrete pile caused by soil piping: an approximate experimental study

2017· article· en· W2768846030 on OpenAlexvenueno aff
Xinzhuang Cui, Jiong Zhang, Dar-Hao Chen, Shucai Li, Qing Jin, Yingjie Zheng, She-qiang Cui

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersScience, Technology and Innovation Commission of Shenzhen Municipality
KeywordsCloggingHydraulic conductivityGeotechnical engineeringPervious concretePipingPermeability (electromagnetism)PilePorosityMaterials scienceGeologyEnvironmental scienceComposite materialSoil scienceSoil waterCementEnvironmental engineering

Abstract

fetched live from OpenAlex

Due to their high permeability and high strength, pervious concrete piles (PCPs) can improve ground bearing capacity. However, clogging of pervious concrete in practice is a potential disadvantage. To investigate the clogging mechanism of PCPs due to sand piping, a series of laboratory simulation tests is conducted on a developed hydraulic conductivity test system. This testing demonstrates the effects of pervious concrete porosity, grading of fine movable particles, mix ratio of skeleton particles to movable particles, relative density of soil, and distance between PCPs on PCP clogging. The experimental test results show that the hydraulic conductivity of PCP decreases for approximately 70 min and then becomes relatively stable. In addition, it is observed that PCP clogging rarely occurs in cases of low pervious concrete porosity, small movable sand particle size, high sand relative density, and large pile distance. The results also show that measurement of electrical conductivity can be an alternative method of hydraulic conductivity measurement. Based on the test results, preliminary clogging models are proposed.

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 categoriesMeta-epidemiology (narrow)
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 score0.228
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.234
Teacher spread0.220 · 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.

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

Citations22
Published2017
Admission routes1
Has abstractyes

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