Development of suction measurement techniques to quantify the water retention behaviour of GCLs
Bibliographic record
Abstract
ABSTRACT: Geosynthetic clay liners (GCLs) have the potential to act as excellent hydraulic barriers, and have been successfully used in numerous barrier system applications, including composite landfill liners. In order to function effectively in the role of a hydraulic barrier, these products must first hydrate through the uptake of moisture from the subsoil. They then must demonstrate adequate dimensional stability during any subsequent moisture loss, to avoid separation of the panel overlaps. The key to understanding these moisture uptake and retention phenomena is the constitutive relationship between suction and moisture content. This relationship is commonly referred to as the water retention curve (WRC) of a material. Despite the significance of this relationship for the final success of the barrier, only a few studies have successfully quantified portions of water retention curves, and for only a subset of available GCL product types. This scarcity of data is due primarily to the inherent difficulty of determining this function experimentally for a composite material such as a GCL, and to the difficulty in measuring the wide range of suctions that need to be investigated. In response to this data gap, a dual-technique strategy for the quantification of WRC for GCLs is investigated in this paper, in which two different suction measurement techniques (high-capacity tensiometers and capacitive relative humidity sensors) have been assessed to see whether they are capable of experimentally quantifying the relationship between moisture content and suction for a GCL. This paper discusses the sample preparation techniques and required equilibration times for these techniques, and demonstrates that they can provide water retention data for GCLs that are consistent with published results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".