Modelling the Deterioration of Bridge Decks Based on Semi-Markov Decision Process
Bibliographic record
Abstract
Deterioration models represent a very important pillar for the effective use of bridge management systems (BMS's). This article presents a probabilistic time-based model that predicts the condition ratings of the concrete bridge decks along their service life. The deterioration process of the concrete bridge decks is modeled using a semi-Markov decision process. The sojourn time of each condition state is fitted to a certain probability distribution based on some goodness of fit tests. The parameters of the probability density functions are obtained using maximum likelihood estimation. The cumulative density functions are defined based on Latin hypercube sampling. Finally, a comparison is conducted between the Markov Chain, semi-Markov chain, Weibull and gamma distributions to select the most accurate prediction model. Results indicate that the semi-Markov model outperformed the other models in terms of three performance indicators are: root-mean square error (RMSE), mean absolute error (MAE), chi-squared statistic (x2).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".