Three-Dimensional Seismic Analysis of Tsankov Kamak Dam
Bibliographic record
Abstract
Abstract This paper presents the finite element analysis of Tsankov Kamak Dam, a double-curvature concrete arch dam in the Rhodope Mountains of Bulgaria, conducted as part of Theme A of the 17th ICOLD Benchmark Workshop on Numerical Analysis of Dams. The theme includes six case studies (Cases A to F), covering static, modal, foundation dynamic, linear dynamic, and non-linear dynamic analyses. Five subcases are mandatory for a systematic performance assessment of the three-dimensional Dam-Foundation-Reservoir (DFR). This study evaluates all five mandatory subcases along with one optional subcase, providing insights into the seismic performance of the DFR system. The model was reconstructed from the provided orphan mesh using HyperMesh, and numerical simulations were carried out in Abaqus 2022 with benchmark material properties, boundary conditions, loading schemes, and seismic inputs. For Case D (Linear Dynamic Analysis with massed foundation), free-field motions were deconvoluted using DEEPSOIL v7 to ensure realistic wave propagation at the foundation base. Symmetric boundary conditions were used in the static analysis, while dashpot boundaries were applied in the dynamic cases to absorb outgoing energy and simulate free-field conditions. The results demonstrate that foundation flexibility and inertia significantly influence crest response and stress distribution, while reservoir coupling reduces natural frequencies and alters hydrodynamic pressures. Case D produced physically consistent accelerations and displacements with proper damping decay, while Case F (massless foundation) confirmed the inadequacy of neglecting foundation mass for stress evaluation despite comparable displacement trends. These findings validate the necessity of full DFR modeling in seismic safety assessments of arch dams and underscore the effect of model complexity on predicted dynamic behavior.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".