Constitutive modelling approach for evaluating the triggering of flow slides
Bibliographic record
Abstract
The paper presents a methodology to evaluate flow slide susceptibility in potentially liquefiable sandy slopes. The proposed approach accounts for both contractive and dilative volumetric behaviour during shearing using the MIT-S1 constitutive model. As a result, it is possible to distinguish among different types of undrained response induced by a rapid shear perturbation. The first part of the paper describes the general methodology for infinite slopes, providing an index of stability for incipient static liquefaction in shallow deposits. The methodology accounts for the anisotropy due to the initial stress state and uses simple shear simulations to assess instability conditions as a function of slope angle, stress state, and density of the soil. The resulting stability charts define the margin of safety against static liquefaction and the depths likely to be affected by the propagation of an instability. The second part of the paper applies the methodology to the well-known series of flow failures in a berm at the Nerlerk site. The MIT-S1 model is calibrated using published data on Nerlerk sands and in situ cone penetration test (CPT) data. The analyses show that in situ slope angles α = 10°–13° are less than the critical slope angle needed for incipient instability. Liquefaction and flow failures were therefore promoted by small perturbations in shear stresses that could be generated by rapid deposition of hydraulic fill.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".