Centrifuge modeling of shallow embedded foundations subjected to reverse fault rupture
Bibliographic record
Abstract
Although the performance of surface, piled, and caisson foundations has been investigated against a large tectonic dislocation from a dip-slip fault, to date, the embedment depth has not been clearly considered on the behavior of shallow foundations subjected to dip-slip faulting. This paper presents a series of centrifuge model tests to investigate the effects of foundation embedment depth and contact pressure on the interaction of reverse faults and shallow foundations embedded at a depth of D. The effect of embedment depth on the behavior of a foundation was observed by comparing the results of the embedded foundation tests with those of surface foundation tests. The depth of the embedment, acting as a kinematic constraint, prevents the occurrence of sliding at the foundation–soil interface and consequently leads to significant foundation rotation and translation. Moreover, embedding the foundation causes the mechanism of the fault rupture – foundation interaction to change. The effect of contact pressure on the interaction of the fault rupture and the embedded foundations depends on the foundation position relative to the fault. In addition to the propagation of fault ruptures through the soil layer, passive failure wedges primarily occurred on both sides of the embedded foundations because of their translation and rotation, thereby imposing unfavorable effects on the adjacent structures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".