Dynamic characteristics assessment of the Denis‐Perron dam (SM‐3) based on ambient noise measurements
Bibliographic record
Abstract
Abstract Standing at a height of 171 m in a narrow valley, the Denis‐Perron dam is the highest embankment dam in Québec, Canada. Between 1998 and 2002, the structure was equipped with three‐component digital strong‐motion accelerometers. The seismic data recorded during three small earthquakes allowed for the estimation of the fundamental vibration frequency (fn) values of the dam in three directions. As part of the present research study, 10 sets of ambient noise measurements were acquired at the dam using six velocimeters, aiming to evaluate the possible 3D site effects and determine the fn values. The first analyses of these experimental data consisted of processing the individual signals with the simple “reference‐dependent spectral” and “ambient vibration horizontal‐to‐vertical spectral ratio (HVSR)” methods. A more advanced modal analysis combining synchronized measurements led to the establishment of additional higher vibration frequency modes and modal shapes. The consistency between the fn values obtained using the simple methods and those from the advanced modal analysis was noticeable. The seismic data provided an exceptional opportunity to validate the experimental data. A comprehensive analysis was thus conducted to compare the dynamic parameters calculated from the processed experimental data with those resulting from the seismic data and proved the values to be similar. In addition, a numerical modal analysis of the 3D valley‐dam system corroborated the plausibility of the vibration modes obtained from the experimental synchronized ambient noise modal analysis. All of these analyses demonstrate the potential of the ambient noise technique for identifying the dynamic characteristics of large rockfill dams built in narrow valleys, without the need for the costly installation and maintenance of accelerometers in low‐seismicity areas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".