Identifying climatic hazard importance factors for bridges using expert-based fuzzy analytic hierarchy process
Bibliographic record
Abstract
Bridges are critical components of ground transportation infrastructure, yet current design provisions remain rooted in historical climate assumptions that diverge sharply from projected future conditions. As climate change progresses, particularly under higher-emission scenarios such as RCP8.5, infrastructure managers require a systematic and quantitative basis for identifying hazards that pose significant risk to bridges and for prioritizing adaptation measures. This study develops and validates an adaptive fuzzy analytic hierarchy process (FAHP) protocol for deriving climatic hazard importance factors (CHIFs) across a three-tier hierarchy that links four hazards (temperature, wind, rainfall, and ice accretion) to bridge systems and components. Expert judgment was elicited from structural engineers, researchers, and asset managers through a fuzzy pairwise comparison method. For a representative simply supported concrete bridge, rainfall emerged as the most critical hazard (CHIF = 0.35), followed by temperature (0.26), wind (0.22), and ice accretion (0.17). The protocol produces CHIFs not only at the overall bridge level but also at the system and component levels, enabling targeted adaptation strategies—for example, drainage improvements for decks (CHIF = 0.48) and cold-weather sealing for expansion joints (CHIF = 0.34). The methodology offers four key contributions: (1) a replicable, uncertainty-aware framework for multi-hazard weighting applicable to any geographic context, (2) quantitative inputs for revising Canadian Highway Bridge Design Code load combinations, (3) decision support for prioritizing the rehabilitation of climate-vulnerable components, and (4) network-level asset ranking to guide limited adaptation budgets. By translating expert insight into scale-ready metrics, the protocol bridges the gap between climate projections and engineering decision-making. • Develops AHP hierarchy for bridge risk from temperature, wind, rain, ice. • Surveys 8 Canadian bridge experts across systems and components. • 46 pairwise comparisons capture hazard, system, and component weights. • Aggregates judgments via geometric mean; CR ≤ 0.088 across levels. • Provides prioritized weights to guide climate-aware bridge design/asset mgmt.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".