Experimental study of damping in civil engineering structures using smart materials (NiTi SMA). An application to stayed cables for bridges
Bibliographic record
Abstract
Experimental study of damping in civil engineering structures was done using smart materials (NiTi SMA). The study is focused on the stayed cables for bridges. The cable oscillations are induced by wind, rain or traffic. The SMA and their solid-solid phase transformation permits effective damping via the hysteresis cycle and the conversion of mechanical work in heat. The analysis of SMA behavior needs to be strictly coherent with the requirements associated to the application. In particular, the high requirement on working cycles induced by a strong storm of 3 or 4 days, the temperature stress dependence of the phase transformation (via Clausius-Clapeyron equation), the self-heating effects, the SMA creep and the eventual effects of aging (temperature and time) on the transformation temperatures (i.e., atomic order and/or other effects) were studied. The SMA was applied to mitigate the oscillations induced in realistic cables in ELSA (45 m length) with positive results, and in the LCPC facility (50 m length), always with equivalent simulation tests. The experimental examples suggest that the SMA permit an effective reduction of the oscillation amplitudes better than a half. The work is realized in the frame of SMARTeR, one S3T-ESF project their goal is the damping in stayed cables of bridges. For instance, the Iroise bridge in the highway between Brest and Plougastel, France, or eventually in the St. Nazaire bridge in the Loire Atlantique near Nantes or other bridges (i.e. the Viaducs du Boulonnais in the A16 highway, France).
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".