Mitigation of liquefaction hazard by dynamic compaction — a random field perspective
Bibliographic record
Abstract
This paper presents the findings of a case study to quantitatively assess the effect of dynamic compaction (DC) on mitigating liquefaction hazards from a random field perspective. DC is known to increase the density and strength of loose sand deposits, leading to a decrease in liquefaction potentials. Thus, by comparing the liquefaction potentials before and after DC at a given site, the effectiveness of DC in mitigating liquefaction hazards can be quantified. In practice, however, a direct one-to-one comparison is challenging due to limited availability of in situ test data and the fact that the number and location of these data before and after DC are typically different. To overcome these challenges, a random field-based approach is proposed in this study to visualize and quantitatively evaluate the effectiveness of DC across the entire project site. This approach is proven effective in assessing the effects of DC and is validated with liquefaction observations from the 1999 Chi-Chi earthquake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".