Adopting Robotic Systems to Enhance Vibration Control of Footbridges
Bibliographic record
Abstract
With innovation and aesthetics playing leading roles, the landscape of footbridge design continues to evolve towards lightweight, landmark structures, increasing the emphasis on mitigating lively dynamic responses. To satisfy architectural constraints, auxiliary control devices are being used to control the response under infrequent peak-loading events. Nearly all implemented control devices are permanent installations of passive systems, such as viscous dampers or tuned mass dampers (TMDs), that are tuned to a particular structural property and hence specific to the particular application. Recently, the concept of deployable, autonomous control systems has been presented, in which a robotic platform is combined with an active control device to yield a system that is capable of providing short-term control for a range of structures. This concept is particularly attractive in relation to footbridge applications, where the systems can be deployed during predictable peak-loading events such as marathons or used with temporary footbridges where the need for control depends on the intended use. In this paper, analytical modeling of a prototype system is presented to validate experimental identification. Furthermore, the role of the control–structure interaction (CSI) is described and compensated for through an active controller formulation and the use of a position feedback controller for disturbance rejection. The performance of the prototype system is evaluated experimentally and assessed relative to an equivalent passive TMD device. The experimental study validates the controller formulation and demonstrates the effectiveness of the prototype system.
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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 | 0.001 | 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".