3D dynamic response analysis of the Denis-Perron Rockfill Dam in a narrow valley
Bibliographic record
Abstract
Seismic analysis of rockfill dams in narrow valleys poses challenges in representing material behavior, complex wave propagation, and three-dimensional (3D) site effects. This study addresses these issues through a combined parametric and validation analysis of the Denis-Perron Dam—the largest rockfill dam in Eastern Canada—using instrumental data from earthquakes and ambient vibrations. Progressive modeling in 1D, 2D+, and 3D approaches shows that valley geometry strongly influences seismic response when the width-to-height ratio ( L/H) is below 4. For the Denis-Perron Dam ( L/H = 2.21), 3D simulations produced crest amplification factors of 6–8 at the fundamental frequency (∼2 Hz) under nonlinear conditions, compared to 4–5 in 2D analyses, with site effects concentrated in the upper third of the dam. Validation against records from the 1999 M5.1 earthquake confirmed the predictive capacity of the 3D model, while simulations of a historical M5.9 event indicated that high-frequency motions (>10 Hz) typical of Eastern Canada reduce—but do not eliminate—significant side effects. Beyond the technical results, the study demonstrates the feasibility of advanced 3D dynamic modeling when supported by a structured development and validation process and underscores the critical role of high-quality seismic instrumentation both for dam safety monitoring and for understanding complex dam–valley interaction phenomena. These findings inform seismic design criteria, safety evaluation, and instrumentation strategies for large rockfill dams in complex geological settings.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".