Shear strength characteristics of interlocked EPS-block geofoam-sand interface
Bibliographic record
Abstract
Translation of entire geofoam embankment at the bottom of geofoam block assemblage and bedding sand is a possible internal failure mode due to unbalanced hydrostatic forces. In addition, available shearing resistance along the bottom of geofoam block assemblage and bedding sand is an important design issue for internal seismic stability analysis. This study has focused on the methods to increase the interface shear resistance along the traditional flat surface geofoam block and bedding sand interface. In addition to traditional flat surface geofoam-sand interface, the effect of four different interlock configurations composed of geofoam blocks with one- and four-triangular and one- and four-square ledges were quantified by using direct shear tests. Two different densities (EPS19 and EPS29) and two different types of sand (Ottawa sand and Adapazari sand) were used. Therefore, the effects of geofoam stiffness, interface geometry, grain size and particle shape of the bedding sand on the interface stress-strain behavior was quantified. Manufacturing ledges along the traditional geofoam surface significantly improved the geofoam block-sand interface shear resistance. In addition, interrupting the failure plane with ledges changed the interface shear mechanism of the traditional flat surface geofoam block-bedding sand interface from purely frictional to frictional-cohesive behavior.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".