Modelling inspection and fatigue retrofitting by post-weld treatment in bridge management systems
Bibliographic record
Abstract
Bridge management systems (BMSs) are routinely used to model the deterioration of bridge components due to manifest processes, such as deck surface wear and corrosion. Fatigue deterioration and retrofitting are generally not modelled in BMSs, however, primarily due to the difficulties associated with detecting fatigue damage at its early stages. In this paper, a simple Markov chain fatigue model, similar to those commonly used in BMSs to model other deterioration processes, is described and evaluated by comparison with a previously validated probabilistic mechanistic (strain-based fracture mechanics) model. Several alternative fatigue management strategies employing various combinations of weld inspection (magnetic particle inspection) and retrofitting by post-weld treatment (needle peening) are analyzed. Based on the results of these analyses, conclusions are drawn regarding the suitability of the Markov chain model for making lifecycle cost comparisons of fatigue management strategies employing inspection and post-weld treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".