Design of geomembrane specimen for biaxial tensile testing
Bibliographic record
Abstract
A numerical–experimental framework is developed to optimise the design of geomembrane specimens for biaxial tensile testing. Specifically, the geometry and adhesive bonding method of specimens are optimised to maximise strain in the central measuring region while reducing premature failure caused by stress concentrations outside this region. Numerical simulations are used to generate stress distribution datasets, and an objective function integrating two metrics—stress concentration and load transfer efficiency—was used to evaluate specimen geometries. Four key geometric parameters are optimised using a derivative-free subdivision method. Specimens are reinforced by adhesive bonding outside the central region, with bonding methods optimised through uniaxial tensile tests to minimise the effect on inherent material properties. Biaxial tensile tests are conducted to validate and compare three promising geometries obtained from numerical optimisation. The final optimal specimen design achieves a peak central strain of 160.2%, significantly exceeding the previous results. This optimised specimen design provides a valuable basis for future studies on geomembrane biaxial tensile testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".