Integration of SHM at an early stage in the design and construction of long-span bridges
Bibliographic record
Abstract
Automated monitoring systems are being increasingly used on long-span bridges to address a wide range of challenges, such as those encountered during the construction stage or those associated with maintenance and life-cycle optimization. Bridge designers are now more prepared than in the past to consider the use of SHM systems in their work from an early stage, and to support contractors in implementing such systems during the construction stage. Close coordination between bridge designers, contractors and SHM specialists enables the appropriate equipment to be integrated wisely in the construction process, and ensures that full advantage may be taken of the benefits that can be gained from the use of an SHM system, right from the start of the bridge’s life cycle. This can be particularly important, for example, where components of the SHM system require to be embedded in a structure’s concrete during the construction stage, or where the system will play a significant data measurement and assessment role in the construction process as a whole. This is illustrated with reference to current bridge construction projects in India and Canada.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".