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.
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.
How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".