Protection barriers impacted by multiple surges of flow-like landslides: A Material Point Method numerical study
Bibliographic record
Abstract
Flow-like landslides propagate fast and often as a sequence of surges. The design of protection barriers must be based on an accurate estimate of flow discharge, potentially making a difference in the barrier performance. The flow-barrier interaction mechanisms are influenced not only by the total amount of material, but also by the timing of the surges, which are expected to interact with each other both before and during the impact of the first mass on the barrier and during later stages. A framework is proposed here as a reference for performing advanced large-deformation numerical analysis. The Material Point Method (MPM) is used for its capability to accommodate large deformation scenarios, with distinct material properties while still recurring to classical concepts of geotechnical engineering implemented in traditional Finite Element Method (FEM) approaches. The case of geosynthetic-reinforced barrier is considered. The results of MPM are presented for a free barrier (in its original configuration) compared to those scenarios where the barrier is partially or entirely buried, for instance, due to long-term lack of maintenance and/or short-term previous flow event with a soil deposition just behind the barrier. The results show that multiple surges lead to a more gradual dissipation of kinetic energy compared to a single, larger flow of equivalent mass. This is due to significant energy loss from inter-surge collisions before the full impact on the barrier, resulting in a smaller final barrier displacement. The study also explores scenarios with a pre-existing deposit behind the barrier, as in long-term conditions, and analyzes how such deposit alters impact dynamics and energy dissipation patterns. This framework provides a reference for advanced numerical analysis in the performance-based design of protection barriers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".