Performance of GCLs in brine ponds for coal-seam gas extraction sites: An investigation
Bibliographic record
Abstract
Systems made of a geosynthetic clay liner (GCL) underlying a geomembrane (GMB), have been widely and successfully used to minimize the escape of pollutants into groundwater. They are also being used for brine ponds at coal seam gas extraction sites as well as solar ponds where temperatures can be reach 70-93oC and only a small effective stress is applied (typically < 50 kPa). A failure of the liner system in these facilities could cause serious environmental damage. However, little is known about how the GCLs are affected by temperature increases, especially in the presence of salt which, if in direct contact with the GCLs, can lead to a significant increase in their hydraulic conductivity. Historically, GCL design has largely evolved empirically, with relatively little theoretical work guiding best practice. Hence, serious questions about GCL viability and long-term performance remain. The only model available for assessing GCL desiccation risk was developed for applications with stresses exceeding 50 kPa, in 1D, based on a theory that does not include the GCL's thermoplasticity, the effects of salt gradients, or defects in the GMB; nor is it based on water retention curves (WRC) that are most suitable for GCLs at low normal stress. The paper has two objectives: a) to describe a current project aiming to develop an experimentally-validated theory of multi-phase thermohydro- mechanical behaviour of GCLs under low stress and high temperature; b) to report an investigation into the ability of the computer software Code-Bright to simulate the hydrationdehydration dynamics of GCL systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".