Predicting the Remaining Useful Life of Corroding Bridge Girders Using Bayesian Updating
Bibliographic record
Abstract
This paper developed a model for predicting the temporal failure probability of prestressed concrete (PC) highway bridges. The model updates predictions based on data from nondestructive testing and visual inspections. Chloride-induced corrosion is taken as the main cause for deterioration, and a gamma process describes the reduction in structural capacity. A nonhomogeneous Poisson process models vehicle arrival, in which the vehicle load variability follows a two-peak lognormal mixture distribution. After each inspection, Bayesian inference updates select model parameters and, consequently, the associated temporal structural resistance. Bridge managers can use the updated failure probability predictions to evaluate the remaining useful life of the bridge and determine the maintenance scheme and budget accordingly. A real bridge example illustrated the methodology and justified the probability distributions used for deterioration and vehicle load. Through comparisons with three existing methods, we argued that the proposed model provides more-conservative recommendations, yet existing methods tend to underestimate failure probabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".