Back-analysis of geophysical flows using three-dimensional runout model
Bibliographic record
Abstract
Predicting the mobility and delineating the extent of geophysical flows remains a challenge for engineers. The accuracy of predictions hinges on the reliability of input parameters of runout models. Currently, limited field data for landslide case histories are available for benchmarking the performance of runout models. Key rheological parameters, such as the equivalent internal friction angle, cannot be measured directly using laboratory experiments and must instead be determined through back-analyses. A series of dynamic back-analyses was carried out for notable landslide case histories in Hong Kong, accounting for the effects of pore-water pressure on the equivalent internal friction angle, using a three-dimensional finite-element mobility model. The recorded and simulated run-out distances, as well as lateral spreading, were compared. Results reveal that the back-analysed equivalent internal friction angles resulting from open-hillslope failures and from channelized geophysical flows range from 25° to 30° and 15° to 20°, respectively. This is attributed to incised geophysical flow channels having an elevated water head and higher degree of saturation compared to open-hillside slope surfaces, wherein the induced elevated pore-water pressure profoundly lowers the equivalent internal friction angle. The back-calculated values may be useful for finite-element-based design of mitigation measures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".