Seismic assessment of sheet pile reinforcement effect on river embankments constructed on a soft foundation ground including soft estuarine clay
Bibliographic record
Abstract
Conventional seismic assessments of river embankments have focused on liquefaction damage of sandy ground. However, following the 2011 earthquake centered off the Pacific Coast of Tohoku, extensive damage of river embankments built on a clayey foundation has garnered greater attention. This paper presents seismic response analyses of river embankments constructed on soft and sensitive estuarine clay, as well as analyses of countermeasures implemented with sheet piles with a succession of penetration depth and placement. River embankments have kept stable if the clayey ground was assumed to be a nonsensitive condition. However, if a strong shake impacts actual sensitive clay, there is a risk of slippage generated from the clayey layer. If the penetration depth of piles is shallow in the clayey layer, the clay is strongly disturbed by the tip of the pile, generating extensive damage; if the penetration depth is sufficiently deep in the supporting layer, significant deformation control may be obtained at the reinforced side, although there is a risk of promoting deformation at the opposite side. These results indicate the importance of the specific inspection of pile penetration depth; otherwise, not only is it not possible to obtain a sufficient countermeasure effect, but also an adverse effect may be generated.
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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 | 0.000 |
| 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) | 0.001 | 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".