Adopting Robotic Systems to Enhance Vibration Control of Footbridges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it