Monitoring for Durability and Structural Behavior of Medium and Long Span Concrete Bridges
Bibliographic record
Abstract
The ageing and deterioration of highway bridges can have very serious consequences in terms of reduced safety, serviceability and functionality. Many bridges built in the 1960's and 1970's are considered deficient by today's standards. The widespread deterioration and some recent failures have highlighted the importance of developing and implementing effective inspection strategies, including structural health monitoring systems, which can identify structural problems before they become critical and endanger public safety. Continuous monitoring is becoming necessary due to ageing of bridges, increased traffic loads, changing environmental conditions, and reduced capacities, especially for medium and long-span bridges given the severe consequences of failure. The implementation of monitoring programs can assist in optimizing the in-depth inspection, maintenance, rehabilitation, and replacement of bridge structures. The continuous and simultaneous measurements at critical discrete points of a bridge system will allow the assessment of its performance with respect to different limit states, including safety and serviceability. Prediction models, updated from such monitoring data, can optimize intervention strategies as to how and when to repair or rehabilitate thus extending service life and reducing life-cycle costs.The objectives of this paper are: (i) to present an approach for the efficient use of structural health monitoring into the durability and structural reliability assessment process; (ii) to highlight the applicability of the approach to short, medium and long-span bridges; and (iii) to demonstrate the effective use of field monitoring data for the calibration and updating of service life prediction models. A case study on a medium-span concrete highway bridge is also presented and used to illustrate the approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".