A Comprehensive Asset Management Plan for the Bridges of Ontario for 2023–2025
Bibliographic record
Abstract
Bridges, like any other infrastructure, deteriorate over time, and the more they degrade, the more expensive it becomes to perform maintenance activities on them. Therefore, it is important to predict their deterioration and plan intervention programs from economic and functionality points of view. While deterministic models are used to predict the deterioration of a structure, they do not consider any maintenance activities carried out beforehand. In this paper, we adjusted the deterioration model to overcome those limitations and planned an intervention program for the bridges of Ontario for 2023–2025. To achieve this, we cleaned the dataset; formulated the models; ran simulations with multiple deterministic and stochastic models to find the best one; adjusted the model equation to account for various levels of previous maintenance works; formulated the intervention action requirement for the next three years by developing an algorithm to reflect previous maintenance trends and their effects on the condition ratings; prepared an equalized work requirement for each year; and prioritized the action requirements based on a proposed risk matrix. Finally, an exhaustive plan with an action requirement for all the bridges was prepared for 2023–2025. The proposed plan will help decision makers to take proper action to maintain the serviceability of the bridges.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".