Decision support framework for screening deficient bridges based on statistically-identified key indicators and Monte Carlo simulation
Bibliographic record
Abstract
The urgent need to prioritize deficient bridges in hazard-prone regions highlights the importance of strategic decision-making under financial constraints. This paper develops a comprehensive screening framework that integrates economic and social impacts alongside structural condition indices. First, key performance indicators (KPIs) are statistically identified using the Taguchi design of experiments, providing a data-driven foundation for the model. Next, the Fuzzy Analytic Hierarchy Process (AHP) assigns dynamic weights to the KPIs, emphasizing critical factors such as structural condition and bridge importance. Subsequently, a Bridge Screening Index (BSI) is introduced and applied to a case study of 15 bridges in British Columbia, Canada. Finally, a Python-based algorithm was implemented to conduct Monte Carlo simulations, evaluating the model's sensitivity to variations in input parameters. By building on the simulation outcomes, a refined BSI formulation is suggested. This simplified approach is practical for data-limited scenarios, offering optimal results with a 95 % confidence level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".