Stochastic Modeling Of Infrastructure Deterioration - An Application To Concrete Bridge Decks
Bibliographic record
Abstract
This paper presents an application for the development of stochastic models to predict the deterioration of infrastructure facilities. The main objective is to demonstrate the capabilities and limitations of two types of models, namely state-based and time-based models, which will guide decision makers in selecting the most appropriate model type according to management needs and data availability. Concrete bridge decks are selected for this application because they are considered one of the most deteriorating infrastructure components. Inventory and condition data required for developing the stochastic deterioration models are obtained from the database of the Ministére des Transports du Québec (MTQ). Markov-chain models, as an example of state-based models, and non-parametric time-based models are developed for bridge decks when no maintenance actions are taken. Although this application demonstrates the development of stochastic deterioration models for concrete bridge decks, these models can be developed to predict the performance of other infrastructure facilities for network level analysis. KEY WORDS bridge decks, deterioration models, maintenance management, decision making, stochastic approaches
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".