Factors Affecting Liquefaction Triggering of Granular Soils in Laboratory Testing
Bibliographic record
Abstract
In practice, liquefaction assessment is driven by earthquake case histories, but the case-history database is limited and there are important gaps in the information available. As a result, laboratory tests have continued to play a key role in determining how various factors influence liquefaction triggering. This paper presents an investigation into how factors such as particle size, particle size distribution, and preparation method influence cyclic liquefaction triggering. Eleven particle size distributions of a natural soil and tailings, ranging from pure silt to fine gravel, were tested in cyclic simple shear. The results were cast in the critical state framework and compared with a large body of data from the literature to understand how the choice of testing method influences the cyclic resistance. The results suggest that the widely used constant volume cyclic simple shear tests produce significantly lower liquefaction resistance values than either cyclic triaxial tests or the case history database. Simple shear tests are not as sensitive as cyclic triaxial tests are to a wide range of factors, including overburden stress, particle size, particle size distribution, and specimen preparation method.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".