Possible remediation of impact-loading debris avalanches via fine long rooted grass: an experimental and material point method (MPM) analysis
Bibliographic record
Abstract
Abstract Debris avalanches often originate along steep unsaturated slopes and have catastrophic consequences. However, their forecast and mitigation still pose relevant scientific challenges. This is also due to the variety of mechanisms observed near high sub-vertical bedrock outcrops, such as the impact loading of soil failed upslope the outcrop, the build-up of pore water pressures in the inception zone, and the bed entrainment along the landslide propagation path. At the University of Salerno, an experimental and numerical investigation campaign started some years ago to explore the feasibility of using long-root grass to mitigate or even inhibit the inception of debris avalanches. Previous laboratory results were achieved through two twin 2-m-long columns (one bare, one vegetated), where the change in soil retention curve and soil mechanical response was assessed. As follow-up, an experimental field setup was installed in 2020 first, and in an improved configuration in 2021. Here, three different species of long-root grass were grown. In situ soil suction and water content measurements were periodically collected in the vegetated and in the original soils. In both cases, soil specimens were also collected, and laboratory geotechnical tests were performed to individuate the changes in both the water retention and strength response. Increased values of soil suction and shear strength were outlined, despite some differences, for all the grown species compared to the original soil. Using these novel experimental data, advanced large-deformation stress–strain hydro-mechanically coupled analyses were recently performed through a material point method (MPM) approach. The original slope conditions were compared to various slope configurations engineered via long-root grass. The benefits and the open issues related to this novel green technology for landslide mitigation are discussed. Some insights are outlined for the possible reduction of the soil volumes mobilized inside the inception zone of debris avalanches.
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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".