The Liquefaction and Cyclic Mobility Performance of Embankment Systems Constructed with Different Sand Gradations
Bibliographic record
Abstract
A broad range of coarse-grained soils that vary in gradation uniformities, maximum particle sizes, and absolute densities are prone to liquefaction during earthquake shaking. However, clean, poorly graded sands form much of the liquefaction case-history database, with these soils often serving as the basis for analysis procedures. This paper presents a centrifuge testing program studying if the liquefaction triggering and deformation performance of embankment systems constructed with poorly graded sands universally applies to well-graded soils. Two soils were used in this study, named 100A and 25ABCD, had variations in maximum particle sizes, coefficients of uniformity (Cu), and void ratio extremes. Dense arrays of in-situ porewater pressure transducers and accelerometers indicate that liquefaction was triggered at near unity in the different soils. The 25ABCD soil, with its larger Cu and lower void ratio indices, exhibited stronger dilative tendencies, better preservation of the long period energy of the input motion, and more rapid dissipation of excess porewater pressures. The 25ABCD embankments had lower overall levels of strain at initial liquefaction triggering and accumulated less strain during cyclic mobility. While the two soils were pluviated to the same relative density and subjected to the same level of shaking, the slope surface displacements in the 25ABCD embankments were 60%–70% less than the displacements measured in the comparable 100A embankments. These results support the hypothesis that liquefaction and deformation behaviors depend on soil gradation, and findings from this experimental program can be leveraged for more accurate performance predictions of levees and earthen dams.
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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 | 0.001 |
| 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) | 0.000 | 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 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".