Operational data collection and analysis for a smart 3D-printed footbridge
Bibliographic record
Abstract
Purpose This paper presents the structural monitoring results of the world's first 3D-printed steel footbridge, using a fixed sensor network built into the bridge, to better understand both the behaviour of this novel structure and the way it is used. Design/methodology/approach The bridge was publicly exhibited and then installed for two years in central Amsterdam. The main features of the sensor network installed to monitor its behaviour are described. The bridge's behaviour was studied using a combination of labelled data collected in controlled conditions at the University of Twente and long-term monitoring during normal use in Amsterdam. Findings The data collected show that thermal behaviour can be effectively decoupled from the response of the bridge due to pedestrian loading and that the pedestrian movements captured by camera can be anonymized as coordinates, which can be correlated with the loads and strains produced by those pedestrians. The Pearson correlation condition is used to identify the type of movement on the bridge, effectively distinguishing between heel-drops, running and walking movements. Originality/value The richness of such a dataset is demonstrated, measured using embedded sensors and Internet of Things technology. Analysis of these measurements gives insights into the behaviour of a unique large 3D-printed steel structure and the use of a busy piece of urban infrastructure more generally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".