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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".