Effect of Joint Orientation on Rock Mass Erosion Based on Experimental Results Using a Pilot Plant Spillway Model
Bibliographic record
Abstract
ABSTRACT: Hydraulic rock mass erosion is termed as the gradual removal of intact rock blocks due to excessive erosive forces of flowing water. Understanding the mechanism of this erosion process requires to study the hydraulic pressures on rock block surfaces, especially at the top and bottom of intact blocks. These intricate hydraulic conditions depend on several hydraulic and geomechanical factors. In the current study, the effect of joint orientation on the hydraulic pressures is studied using the pilot plant scale spillway model constructed at Université du Québec à Chicoutimi, Québec, Canada. Two metal boxes were used to vary the orientation of the joints to −45°, 0° and 45° inclinations with respect to the direction of water flow. The 9 blocks are arranged in 3 rows with 3 blocks in each row; the central block is instrumented to measure the hydraulic pressures. The static and dynamic head (Hs + Hd) at the top of block is much higher than that at the bottom of block for 0° orientation arrangement. However, in the case of inclined joints, the pressure at the top of block is found to be almost equal to that at the bottom of block. 1. INTRODUCTION Hydraulic rock mass erosion is considered critical in the design of hydraulic structures in recent years especially in the case of unlined dam spillways. The stability and safe operation of hydraulic structures is compromised with the higher erosion rates of rock (Gardner, 2023) under flood conditions. Historic erosion cases like the Oroville Dam, California (Koskinas et al., 2019; Wahl et al., 2019), Mokolo Dam in South Africa and Copeton Dam in Australia (Pells, 2016), Ricobayo Dam in Spain (Annandale, 2006) and Spaulding Dam in California (George, 2015) are a few examples of disasters that occurred either under normal operating discharges or flows below the design flow rates. These historical examples illustrate the limitations in existing hydraulic erosion hazard assessment including the mechanism and rate of erosion. The widely used erodibility index method (Annandale, 2006, 2012) is prominent in erosion prediction, given its simplicity. However, due to its empirical nature, it cannot provide the details of the failure modes and mechanisms. Additionally, the resistive capacity of the rock mass given by this method does not incorporate all the hydraulic and geomechanical parameters affecting the erosion process. Other recent empirical methods developed by Pells (2016) also suffer from the similar limitations. The rock mass resistive capacity is governed by the material parameters and the joint characteristics such as joint spacing, joint orientation, shape and volume of the block, joint filling, block protrusion and toughness of the block (Annandale, 2012; Boumaiza et al., 2019; Pells, 2016). Cameron et al. (1986, 1990) have presented various geological aspects of the rock mass that affect the erosion process and highlighted the headward migration of knickpoint with the erosion towards the spillway crest. Pitsiou (1990) have explained the importance of various parameters of the rock mass discontinuities on the hydraulic erosion process and the importance of a classification of the geological data into different categories based on the spillway type. The hydraulic erosive power depends especially on the velocity of the flow, the stream power dissipation and the bed shear stress (Coleman et al., 2003; Dubinski, 2009). The complex interaction of the flowing water with the spillway bed rock determines the erosion process resulting in a complex erosion mechanism.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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.
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