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Record W3118272941 · doi:10.1155/2021/8481571

Study on the Process and Characteristics of Clogging for Ceramic Permeable Brick

2021· article· en· W3118272941 on OpenAlexfundno aff
Zizeng Lin, Hai Yang, Huiming Chen

Bibliographic record

VenueAdvances in Civil Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaInstitut Périmètre de physique théoriqueNanjing Forestry University
KeywordsCloggingPermeability (electromagnetism)BrickMaterials sciencePorosityParticle-size distributionEnvironmental engineeringParticle sizeGeotechnical engineeringComposite materialEnvironmental scienceGeologyChemical engineeringEngineeringMembraneChemistry

Abstract

fetched live from OpenAlex

A ceramic permeable brick was selected for study in a device that was designed to fully investigate the process and characteristics of clogging in permeable bricks. In order to evaluate the permeability influenced by clogging, a simulated rainfall was filtered through the permeable brick placed in an innovative device. The macroscopic and microscopic changes in the brick and the filtrate were all measured to fully investigate the causes and process of clogging. Then, the mechanism of clogging in the permeable brick pores was further discussed. The results showed that the clogging risk of permeable brick was extremely high, and it can result in a complete clogging in only 5–10 years under the experimental conditions. The permeability coefficient and porosity both decreased exponentially with the increase in filtrate, which was attributed to the clogging of the internal pore structure due to particle interception. The chord size distribution results stressed that the blockage mainly occurred in the upper layer pores in the range of 0.5–1.5 mm, which is relatively sensitive to clogging due to the particle size distribution in rain water. The particle size distribution of the influent and effluent indicated that the clogging process could completely remove particles larger than 88 µ m but showed variable removal efficiency for particles with sizes of 20–88 µ m. This research offers new insight into the clogging of permeable bricks and provides theoretical guidance for restoring the brick permeability.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.235

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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