A New Stochastic Rockfall Fragmentation Approach for Lumped Mass Simulations
Bibliographic record
Abstract
Abstract This paper presents a novel fragmentation model for rockfall applications, integrating experimental, theoretical, and numerical observations with stochastic predictions. Extensive experiments with artificial spherical rock-like specimens revealed crucial aspects of block fragmentation upon impact, including fragmentation patterns and the relationship between the number, mass, and velocities of fragments relative to the impact velocity. A stochastic fragmentation prediction model, based on the statistical distribution of material properties of both the falling block and the impacted material, was proposed by the authors. Numerical studies using marble spheres led to a model that stochastically accounts for cumulative damage in the energy required to break the falling block. These insights were incorporated into a fragmentation module within the NURock lump mass trajectory simulator. Validation through targeted simulation, sensitivity analysis, and comparison with in-situ tests confirmed the model's accuracy. The proposed approach significantly advances rockfall simulation, impacting the design of rockfall protection structures by considering block fragmentation and cumulative damage from repeated impacts. This enables more accurate predictions of the masses and energy levels of blocks reaching designated areas, which is crucial for designing effective rockfall protection measures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".