Why is it so difficult to reliably measure the pore pressure at the base of a fast landslide in a laboratory flume test?
Bibliographic record
Abstract
Laboratory landslide flume tests provide valuable insights into the mechanics of multi-phase granular flows within highly controlled settings. Past studies have revealed the complex fluid-particle interactions associated with saturated granular flows result in greater mobility than their dry counterparts, being notably faster, further reaching, and experiencing enhanced spreading. The ability to reliably measure the pore pressures at the base of these flows in the laboratory is critical for developing, evaluating, and validating constitutive relationships linking the effects of pore pressure to the mechanisms causing increased mobility. Unfortunately, experience has shown that two identical sensors installed in the base of a landslide flume can yield wildly different responses to the same multi-phase landslide. In this session, we explore an answer to the question “Why is it so difficult to reliably measure the pore pressure at the base of a fast landslide in a laboratory flume test?” using evidence accumulated from ten years of flume testing using the Queen’s University Landslide Flume. In particular, we explore the hypothesis that surface roughness around pore pressure sensor filter elements can influence sensor readings. A unique experimental strategy of simplifying the flow into a single fluid phase is used to validate sensor readings, prior to application in multi-phase flows. Dam-break releases of 600 kg of water at the top of the inclined flume slope are used as a parametric study to provide evidence to support the hypothesis that surface roughness significantly impacts the pore pressure recorded in high velocity flows. These results are then contrasted to observations of releases of multi-phase flows to derive best practices for the reliable measurement of pore pressure in landslide flume tests.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".