Evaluation of Design Provisions for Pedestrian Bridges Using a Structural Reliability Framework
Bibliographic record
Abstract
The design of pedestrian bridges (PBs) is typically governed by the serviceability limit state under human-induced excitation. A comprehensive evaluation of the reliability level achieved in designing for this limit state has not yet been reported. This paper attempts to address this gap for metal structures through a comprehensive structural reliability-based evaluation of design guidelines currently used in North America and Europe. An advanced first-order second-moment (AFOSM) method is used to determine reliability levels under different loading scenarios considering uncertainties in the pedestrian-induced walking loads, structural properties, and comfort limits. The results show that the guidelines do not achieve sufficiency under the design traffic. Moreover, suburban or urban PBs with frequently occurring design traffic densities of 0.2–0.8 pedestrians per square meter achieve very low reliability levels under infrequent traffic densities. Significant disagreement in the reliability levels obtained by the different guidelines is observed. Based on this evaluation, it is proposed that current design provisions be calibrated to a higher reliability index under design crowd densities and that traffic-dependent comfort limits be adopted. The reliability level achieved by incorporating a model error term, previously proposed by the authors to better align model predictions with observations, is also evaluated. The key results from this evaluation show that the uncertainty in the model error term has a positive impact on the reliability estimates; thus, this term can be regarded as deterministic.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".