Dynamic response of a concrete dam impounding an ice-covered reservoir: Part I. Mathematical modelling
Bibliographic record
Abstract
This paper examines the dynamic response of a concrete dam impounding an ice-covered reservoir and subjected to forced-vibration testing. The analytical research presented is a follow-up to an extensive dynamic testing program carried out on a 84-m high concrete gravity dam located in northeastern Quebec, Canada, under harsh winter conditions, including a 1.0- to 1.5-m-thick ice sheet covering the reservoir. One of the major challenges encountered when analyzing ice-dam-reservoir-foundation interaction is modelling the complex nature of the ice and the boundary conditions governing reservoir motion. The problem is further complicated because there are little or no appropriate experimental data and observational evidence relevant to ice-dam interaction processes. Some of these challenges are addressed herein using a two-dimensional analytical approach, which investigates the effects due to ice cover, water compressibility, and reservoir bottom absorption. A frequency-domain substructure method technique is used and a new boundary condition along the ice-cover-reservoir interface is proposed. The technique developed is implemented in a finite element code specialized in the seismic analysis of concrete dams. Numerical results are discussed in the companion paper in this issue. Key words: gravity dams, concrete dams, ice, reservoirs, mathematical models, ice-structure interaction, fluid-structure interaction, forced-vibration testing, finite elements modelling.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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.
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".