Liquefaction assessment by the unit energy concept through centrifuge and torsional shear tests
Bibliographic record
Abstract
The fundamentals of the energy concept to assess the liquefaction potential of cohesionless soils have been formulated in recent years. To examine the validity of this procedure, a series of centrifuge liquefaction tests were carried out using the same soils that were tested previously as part of extensive research conducted on the subject at Case Western Reserve University. A total of 30 liquefaction tests at accelerations of 50g and 60g were conducted on scaled pore fluid saturated models, prepared in a laminar box, representing a prototype soil deposit. The influence of relative density and effective confining pressure, as well as the effect of different grain size distribution on the energy per unit volume required for liquefaction, is studied. Generalized relationships were obtained by performing regression analyses between the energy per unit volume at the onset of liquefaction and liquefaction affecting parameters. These equations are compared with similar ones that were developed previously using torsional shear tests. A rational procedure to determine site response to earthquake loading and liquefaction susceptibility of a soil deposit is verified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".