An experimental investigation of water level effects on the dynamic behaviour of a large arch dam
Bibliographic record
Abstract
Abstract The need for full‐scale dynamic tests, which are recognized as the most reliable method to evaluate a structure's vibration properties, is increasing as new analysis techniques are developed that take into account the complex interaction phenomenons that occur in dam–reservoir–foundation systems. They are extremely useful to obtain reliable data for the calibration of newly developed numerical methods. The Earthquake Engineering and Structural Dynamics Research Center (CRGP) at the University of Sherbrooke has been developing and applying dynamic testing methods for large structures in the past 10 years. This paper presents the experimental evaluation of the effects of the varying water level on the dynamic response of the 180 m Emosson arch dam in Switzerland. Repeated forced‐vibration tests were carried out on the dam during four different periods of the reservoir's filling cycle during a one‐year span. Acceleration and hydrodynamic pressure frequency responses were obtained at several locations while the dam was subjected to horizontal harmonic loading. The variation of the resonant frequencies as a function of the reservoir level is investigated. A summary of the ongoing numerical correlation phase with a three‐dimensional finite element model for the dam–reservoir–foundation system is also presented. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".