Effects of Rock Foundation Roughness on the Sliding Stability of Concrete Gravity Dams Based on Topographic Surveys
Bibliographic record
Abstract
This paper investigates the effects of rock foundation roughness on the shear strength of dam–rock interfaces and dam sliding stability. For this purpose, bathymetric and light detection and ranging (LiDAR) surveys of existing rock foundation surfaces were carried out close to existing dam sites and processed to obtain realistic dam–rock interface geometries differing by their roughness. The generated rock profiles are implemented into nonlinear finite-element models to conduct stability analyses of two gravity dams differing by size. A detailed analysis of the nonlinear response of dam–rock interfaces is presented in terms of limit friction angles, sliding safety factors, dilation angles, apertures, dam displacements, and shear stresses. It is shown that global roughness along dam–rock interfaces can substantially increase their shear strength. Natural local shear keys at a dam–rock interface may greatly improve the sliding stability of gravity dams; however, their effect is found to be sensitive to dam size and rock mechanical properties. Roughness effects on shear strength are found to generally decrease for larger dams. The results also reveal that the influence of rock strength parameters is more significant when the rock foundation surface includes prominent natural shear keys.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".