Flow failure assessment for dams and embankments
Bibliographic record
Abstract
A procedure is proposed to assess whether a liquefied strength should be applied to a zone of non-plastic silt, silty sand, and (or) clean sand in a static or seismic stability analysis to assess the flow failure potential of dams and slopes. The procedure consists of the following five main steps to assess flow failure potential: (1) assess static liquefaction potential of segments along a potential failure surface; (2) assess seismic liquefaction potential by calculating the factor of safety against liquefaction (FoS Liquefaction ) for any amplitude of shaking in each segment; (3) if liquefaction is not triggered in any of these segments, assess the magnitude of shear-induced pore-water pressures due to seismic or vibratory events of any amplitude; (4) assign a liquefied strength to segment(s) that experience seismic liquefaction, i.e., FoS Liquefaction < 1 or significant pore-water pressure generation, i.e., total pore-water pressure ratio ≥ 0.7; and (5) conduct a post-triggering stability analysis to assess flow failure potential. This procedure is illustrated using the 1971 seismic permanent deformations of Upper San Fernando Dam and 2015 Fundão tailings dam failure.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".