Analysing slope stiffness effects on rockfall fragmentation using a new stochastic rockfall fragmentation approach for lumped mass simulations
Bibliographic record
Abstract
ABSTRACT: A stochastic fragmentation approach for rockfall applications, based on experimental and numerical observations, has been developed. Extensive experimental campaigns on artificial spherical rock-like specimens revealed key aspects of block fragmentation upon impact, including patterns and relationships between fragment characteristics and impact velocity. The authors proposed the first stochastic fragmentation prediction model, based on the statistical distribution of material properties of both the impacting and impacted materials. Numerical studies using marble spheres suggested a model to consider cumulative damage (caused by previous impacts) in the energy required to break the falling block. These findings were incorporated into a lumped mass trajectory simulator, NURock, developed at The University of Newcastle. This paper presents the application of the new fragmentation approach with a focus on the influence of slope stiffness on the rockfall fragmentation outcome. The simulations using NURock are compared to unfragmented simulations using RocFall2. The results showed how harder rock slopes resulted in more rockfall fragmentation, hence less energy and shorter run-out distances. The results demonstrate that accounting for block fragmentation and cumulative damage can significantly alter the design of protection structures, as the size of blocks reaching a given risk zone can be much smaller compared to unfragmented simulations.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".