The Influence of Partial Clogging and Pressure on the Behaviour of Geotextiles in Drainage Systems
Bibliographic record
Abstract
Nonwoven geotextiles have been used for drainage and filtration in geotechnical engineering works for many years. Concerns related to drainage capacity and clogging potential still remain as factors that restrain a broader use of geotextiles for drainage systems, particularly in major engineering projects. This paper presents the test results of the hydraulic characteristics of partially clogged geotextiles under pressure. Partial clogging can occur during spreading and compaction of soil on geotextiles or throughout the service life of the drainage system. Geotextile specimens, artificially clogged in the laboratory and exhumed from actual field works, were tested to assess their normal and longitudinal permeabilities under different levels of soil impregnation and normal stresses. The results obtained showed that partial clogging significantly influenced the mechanical and hydraulic characteristics of nonwoven geotextiles and that soil impregnation was not necessarily detrimental to the geotextile longitudinal permeability under stress. Comparisons of test and predicted results, confirmed that the expression reported by Giroud in 1996 is a useful tool for the prediction of nonwoven geotextile permeabilities under virgin and soil impregnated conditions. Data on the impregnation levels of geotextile specimens exhumed from actual field works are also presented and discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".