Improving performance prediction of corroding concrete bridges with field monitoring
Bibliographic record
Abstract
Continuous monitoring of critical aging bridges has become necessary due to increased traffic loads, changing environments, extreme shocks, which can reduce their load-bearing capacities, modify their failure modes, and increase their risks of failure.Implementation of monitoring programs can provide valuable information on physical health of bridges and their risks of failure.Prediction models updated with monitoring data can help decision makers optimize inspection, maintenance, and rehabilitation of bridges, thus extending their service life and reducing their life-cycle costs.This paper provides an approach based on the monitoring of life cycle performance of concrete bridges exposed to chlorides, and demonstrates its application in a case study.It is first shown that some of the data, which are commonly used by engineers as input values into service life prediction models, can be different from actual field values, because these parameters vary widely in space and time.It is then demonstrated that service life predictions can be improved by updating the models with field monitoring data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".