A Review on Existing Methods to Assess Hydraulic Erodibility Downstream of Dam Spillways
Bibliographic record
Abstract
In recent years, rock scouring or erosion downstream of dams has become an increasing dam safety concern. Several theoretical, semi-theoretical, semi-analytical and numerical methods can be used to assess the rock erosion in hydraulic structures. Semi-theoretical approaches determine the correlation between the erosive intensity of fluid flow and the resistive capacity of rock. Such approaches establish the scour thresholds as a function of erosive intensity of water and several rock mass indices by using in situ data and a curve-fitting approach. In some studies, the excavatability index, initially developed for rock mass stability analysis, was used to analyse the rock mass resistance in hydraulic erodibility analysis. The effectivity and weight of the geomechanical parameters used are yet to be determined on the basis of the erodibility phenomenon. The semi-analytical methods are developed on the basis of the mechanical and hydraulic interaction of rock mass and water. Four methods developed by Bollaert et al. are important in determining the erodibility in the plunge pool, but they are not applicable in the case of spillways. They used the comprehensive fracture mechanics for closed-end joints, quasi-steady impulsion, and dynamic impulsion (DI) for blocky rock erosion. The application of these methods to each site is necessary to identify constants that are difficult to determine. Few numerical methods are available to assess the rock mass erosion in hydraulic structures. In the case of numerical methods, the erosive agent is indistinct, and the hydraulic hazard parameter on the spillway surface is almost challenging to apply. This study comprehensively reviews the mechanism of erosion and the methods for assessing the risk of potential rock mass erosion downstream of dams and hydraulic structures. The advantages and disadvantages of all methods are discussed and the potential future research directions in this domain are proposed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".