Strain-softening model evaluating geobelt–clay interaction validated by laboratory tests of sensor-enabled geobelts
Bibliographic record
Abstract
The interaction between geosynthetics and soil is vital for the stability and the bearing capacity of geosynthetic-reinforced soil structures. This contact behavior between geosynthetics and granular soils has been extensively studied in the literature while there is scarcity of it related to geosynthetics and cohesive soils particularly with softening responses. This paper presents a strain-softening model of geobelt–clay interaction based on direct shear test results under two compaction degrees. A theoretical model for evaluating the pullout behavior of a geobelt is proposed by employing the strain-softening model verified by direct shear tests and a hyperbolic model capturing the stress–strain curves of a geobelt calibrated by uniaxial tensile tests. The proposed model is numerically solved and validated by pullout tests. A kind of sensor-enabled geobelt (SEGB) was adopted in all the aforementioned tests. Both test and numerical results show an overall softening trend in terms of front pull-out force versus displacement. Generally, the model proposed can give reasonably good agreement between calculations and test data during the whole pull-out range. Also, the strain distributions measured by SEGBs demonstrate the working process during the pullout tests, which makes SEGBs a potentially new choice for the strain measurements of in-soil geobelts.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".