Implementation of Structural Health Monitoring for Movable Bridges
Bibliographic record
Abstract
This paper discusses how the current practice of bridge inspection and assessment can be complemented with long-term structural health monitoring (SHM) data within a bridge asset management framework. A brief discussion of asset management is presented with data used in current practice and complementary data from SHM. To use any data in a timely and effective manner requires an information management system that considers all constituents, from bridge owners to decision makers to users. Thus a multilayered bridge information system that considers multiple end users and includes a broad range of data sources is presented. After discussion of the proposed system, the paper presents long-term SHM data and inspection and maintenance data from a movable bridge in Florida. Dynamic data from long-term monitoring were analyzed with time and frequency domain methods as well as statistical methods, which were feasible with large amounts of data. The results of the analysis showed that certain anomalies at critical components of the bridge (e.g., span-lock and gearbox mechanisms) were captured. It was also shown that the issues identified with SHM data were related to the operation and maintenance of the bridge, as described in its maintenance logs. Integration of monitoring systems with routine maintenance applications could be expected to provide timely and effective bridge management.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".