Multi-hazard Response and Fragility Modeling of Bridges with Shallow Foundations
Bibliographic record
Abstract
In Canada, bridges are susceptible to multiple hazards such as earthquake, scour, flood, vehicular loads, etc. Most riverine bridges, which are significantly more susceptible to scour, have deep foundations. Hence, while there are several studies targeting damage assessment of bridges under multi-hazard scenarios, Limited research has been conducted regarding shallow foundation bridges and their response has remained relatively unknown. This study aims at advancing the knowledge on multi-hazard damage assessment of shallow foundation bridges in Canada. The contributions of this study are: 1) To investigate the elemental and overall response of shallow foundation bridges on multi-hazard scenarios by conducting numerical analysis 2) To propose a probabilistic damage assessment framework for bridges with shallow foundations exposed to seismic, scour, and vehicular loadings to facilitate policy-making toward disaster planning and emergency response, 3) To determine which parameters will affect the overall seismic response of a scoured bridge under vehicular loading and to examine which bridge components might affect the damage state of the bridge, and 4) To investigate the probability of any positive scour effect on the seismic response of the bridge. To this end, an extensive literature review has been conducted on 3D structural modeling, soil modeling, and scour modeling. A verified finite-element model has been developed and analyzed by conducting several numerical analyses based on the literature and novel ideas. Finally, a framework has been created explaining how to develop fragility models for shallow foundation bridges. The framework is showcased using a typical bridge in eastern Canada. The results indicate that although scour notably increases the elemental and overall damage of typical shallow foundation bridges, while the scour depth is lower than the foundation height, the increased damage does not impose a significant threat on the bridge safety. In terms of importance, earthquake, scour, and then vehicular loads have the most effect on the bridge response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".