Wide-Width Strength Test for Nonwoven Geotextiles Without Using Grips
Bibliographic record
Abstract
Development of a simple method of testing nonwoven geotextiles in wide-width testing without using complex grips is presented. The technique has been used mainly on 200-mm-wide samples and has been checked using light 500-mm-wide samples (due to machine testing load limitations on the 500 mm samples). It may be used on any width of geotextile if a testing machine of sufficient capacity is available. The proposed method uses a loop of geotextile that is pulled apart by two bars inserted through the loop. The loop joint is made using heavy duty glue applied by a heat gun. By replacing the glue joint with a seam the method is applicable to testing seams. In order to gauge the accuracy of the method a number of variables were tested. These included two types of polymers, the length of the specimen, the rate of extension, and the direction of extension versus the machine or cross-manufactured direction of the geotextile. The test is quick and the testing technique simple. In the one case where manufacturer's published minimum strength results were available every comparative test obtained exceeded those published results. This shows that the modified methodology has given expected results.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".