Seismic Assessment of Concrete Gravity Dam via Finite Element Modelling
Bibliographic record
Abstract
The failure of large gravity dams during an earthquake could lead to calamitous flooding, severe infrastructural damage, and massive environmental destruction. This paper aims to demonstrate reliable methods for evaluating dam performance after a seismic event. The work included a seismic hazard analysis and nonlinear finite element modelling of concrete cracking for two large dams (D1 and D2, of 35 and 90 m in height, respectively) in Eastern Canada. Dam D1 is located in Montreal, and Dam D2 is located in La Malbaie, Quebec. The modelling approach was validated using the Koyna Dam, which was subjected to the 1967 Mw 6.5 earthquake. This paper reports tensile cracks of D1 and D2 under combined hydrostatic and seismic loading. The latter was generated from ground motion records from 11 sites during the 1988 Mw 5.9 Saguenay earthquake. These records were each scaled to two times the design level. It is shown that D1 remained stable, with minor localised cracking, whereas D2 experienced widespread tensile damage, particularly at the crest and base under high-energy and transverse inputs. These findings highlight the influence of dam geometry and frequency characteristics on seismic performance. The analysis and modelling procedures reported can be adopted for seismic risk classification and safety compliance verification of other dams and for recommendations such as monitoring and upgrading.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".