Large-scale three-dimensional experimental investigation on potential high position landslide‑induced waves in Gushui Reservoir, China
Bibliographic record
Abstract
Abstract The occurrence of landslides in reservoir areas and the potential secondary disasters near dams are characterized by their sudden and catastrophic nature, often limiting the availability of actual measurement data. To address this challenge, prototype physical model test always proves to be valuable method to replicate or reproduce such geological hazards. In this study, we focused on the Meilishi landslide in the Gushui reservoir area as a case study to analyze the potential threat of high position landslide-induced waves under gravity. Based on field investigations and relevant statistical geological data, a large-scale three-dimensional physical model was carried out that integrated the interactions of the landslide, the river, and the dam. With a scale of 1:150, the model had the dimensions of 57, 27, and 8 m. Water level and the maximum sliding velocity into the water were selected as independent variables, leading to a total of 18 experiments. An adaptive landslide motion simulation system based on velocity equivalence and a comprehensive measurement system with tracking technology based on hydrodynamics were independently developed. Those approaches allowed us to reveal the propagation characteristics and attenuation laws of high position landslide-induced waves in a curved channel under various complex conditions. The data showed that the maximum wave run-up height on dam was 17.97 m under the most dangerous working condition (H3C09). Importantly, this value did not exceed the maximum height of dam, indicating a certain level of safety margin for the dam. Combined with the data of different working conditions, the optimal window for landslide risk prevention and control warnings was within 550 s after the onset of landslide instability. The key parameters predicted by the tests, including head wave height, wave run-up height on the opposite bank, wave run-up height on dam, and the propagation times, provided a technical basis and valuable reference for dam engineering design and safety. These results make significant contributions to the prevention and control of similar surges hazard induced by high position landslides around the world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".