Development of a simple test to quantify the consolidation properties of liquefied granular materials
Bibliographic record
Abstract
Liquefaction can have devastating consequences by causing increased mobility of debris flows, tailings dam breaches, and settlement following seismic shaking. Observations on the consolidation behaviour of liquefied soils in 1-g or centrifuge shake table tests have permitted significant advancements in analytical and numerical methods to predict the rate and magnitude of consolidation settlement. However, advanced consolidation models introduce material parameters which are currently difficult to define quickly and at low cost. The objective of this paper is to demonstrate the development of a simple, low-cost test that can be used to quickly estimate the spatially and temporally varying coefficient of consolidation, which controls post-liquefaction settlements. Using the proposed simple setup, experiments were conducted on four uniformly graded sands of varying grain size and on one well-graded mixture. A range of analytical and numerical approaches from literature were assessed for their ability to back-analyse the observed pore pressure dissipation and settlement. Estimated values for the coefficient of consolidation were comparable between models and estimates of surface settlement matched well the experimental results. The simplicity of the proposed test combined with its low-cost and mobile nature, raise significant possibilities for quick estimations of the evolution of the coefficient of consolidation post-liquefaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".