Reliability-based life prediction of aging concrete bridge decks
Bibliographic record
Abstract
This paper presents a probabilistic approach to life prediction of concrete highway bridge decks based on Bogdanoff''s cumulative damage model. The condition of the deck is discretized into a finite set of damage states including no damage, onset of corrosion, crack initiation, major damage and failure. The damage accumulation process is modeled using a stationary unit jump Markov chain in which the probability distribution of damage after a duty cycle is assumed to depend only on the length of the duty cycle and the damage accumulated at the start of the cycle. The service life is determined from the time to reaching the absorbing state, which may successively be redefined as the onset of corrosion, cracking, spalling, etc. The proposed model is simple to use and the statistics of the lifetime and damage states at any given time are easily determined. The model yields a practical tool for the condition assessment, life prediction, and maintenance management of highway bridge decks. An example illustrating the application of the proposed approach is presented.
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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 | 0.000 |
| 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 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".