Dynamic Monitoring Systems for Structures under Extreme Loads
Bibliographic record
Abstract
The dynamic monitoring of civil structures such as buildings and bridges is traditionally approached using acceleration and velocity sensors. When a monitoring program is designed to address concerns related to the level of strain in certain members, fatigue or displacements due to extreme loads, it is sometimes advantageous to measure those parameters directly, instead of deriving them from acceleration or velocity data. Inhomogeneous construction materials such as concrete, stone or masonry require the use of long-gauge sensors to measure strain, since local sensors can provide erroneous data due to local material changes. On the other hand, acceleration and tilt sensors are very useful to capture the global deformations and displacements. The development of long-gauge-length fiber optic sensors can be considered as a useful addition to the toolkit of those interested in the structural dynamics. The system can monitor structures over long periods of time at acquisition frequencies up to 10 kHz and with sub-microstrain resolution. The possibility of obtaining static and dynamic measurements from the same sensor is another advantage of this technology. For acceleration and tilt monitoring, MEMS sensors are now recognized as a powerful and low-cost alternative to more traditional sensor types. Fully-integrated sensors with 3-axis accelerometers and two-axis tiltmeters are now available in a compact packaging that also contain all data acquisition electronics and can be easily deployed in new or existing structures, connecting them directly to an Ethernet network. This contribution briefly introduces fiber optic and MEMS sensing technologies and will than illustrate their application to structural health monitoring through several examples, including the new I35W bridge in Minneapolis USA, the Ile d’Orléans suspension Bridge in Québec Canada and the shaking-table test of a full-scale masonry building reinforced with composite materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".