Debris Flows, Boulders and Constrictions: A Simple Framework for Modeling Jamming, and Its Consequences on Outflow
Bibliographic record
Abstract
Abstract Large boulders can jam in constrictions, both in canyons and man‐made structures (e.g., slit‐dams). Boulder jamming occurs stochastically, and partially governs the outflow rate of debris flow material. Explicit hydro‐mechanical models of boulder‐laden flows are too computationally demanding to study this stochasticity. This study presents a new framework implemented into a numerical program. This program encapsulates basic hydraulic equations and criteria predicting boulder blockage at narrow sections, along with a simple statistical way to compute boulder sizes and numbers. The program applies empirical estimates by experts of the average number of boulders in a deposit to evaluate how equivalent multi‐phase flows would interact with a constriction. The model stochastically generates boulders that can obstruct the constriction(s). The program outputs simple descriptions of the upstream flow level over time, as well as the downstream outlet discharge rate and volume. The model is fast (5–30 s per run), allowing uncertainty propagation analyses of interactions between flows, boulders and constrictions. Interaction between both low‐ and high‐risk flows can be quickly evaluated for different scenarios. For uncertainty propagation, we use possibility theory, which accommodates uncertainties relevant to debris flows. The framework gives practitioners a much‐needed generalized approach for understanding the long‐term behavior of boulder‐laden flows through canyons, or for designing engineered structures. Finally, we apply the model to a field case – the design of a proposed North American slit‐dam – thence elucidating the design requirements for effective flow control. We found that horizontal bars are necessary for dependably controlling outflow from this structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".