Initiation of static liquefaction and the role of<i>K</i><sub>0</sub>consolidation
Bibliographic record
Abstract
The potential for static liquefaction of hydraulically placed sands and silts is now well recognised. A particular category of this type of operation, tailings disposal facilities constructed using the upstream method, has come under increased scrutiny due to the large number of failures of these structures. Although the conditions that render a particular deposit susceptible to potential liquefaction are now well known, being a combination of void ratio and mean effective stress that places the material state above its steady state value, the same cannot be said about our ability to predict the stresses at which liquefaction will be initiated. The concept of a collapse surface, derived from the locus of peak shear stress values from undrained compression tests on isotropically consolidated specimens, attempts to provide a method for predicting the onset of liquefaction. As confirmed in this paper, however, application of the collapse surface concept to actual tailings dam facilities results in factors of safety based on an effective stress approach that are significantly less than unity for facilities that have not failed. On the other hand, shear strength values derived from ultimate state conditions are unconservative, predicting factors of safety significantly in excess of unity for facilities that have failed. A comparison of monotonic undrained triaxial compression tests on both isotropically- and K0-consolidated specimens of gold tailings suggests that the resolution to this dilemma lies in the recognition that a kinematic yield surface, which is a function of the consolidation stress path followed, develops in stress space. The collapse surface derived from undrained loading of K0-consolidated loose specimens is shown to provide a greatly improved capacity for predicting the onset of liquefaction under undrained loading conditions.Key words: static liquefaction, tailings, collapse surface, anisotropic.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".