Risk assessment of bridges due to flood and overloading events in Manitoba
Bibliographic record
Abstract
The challenges posed by Canada's aging infrastructure and limited economic resources have underscored the need for proactive measures to address the vulnerabilities inherent in its bridge systems. This study seeks to develop a comprehensive framework tailored to the evaluation of risk factors associated with flooding and overweight traffic on typical highway bridges in Manitoba. The primary objective of this research is to establish a robust methodology for assessing risk values arising from the combined effects of flooding and overweight traffic on bridge structures. To this end, the study engages in a thorough analysis of historical flood data to pinpoint flood-prone zones. Concurrently, historical data pertaining to Average Annual Daily Traffic Volume (AADTT) and Overweight (OW) percentages are examined to ascertain the frequency of overloading risk for bridges due to overweight traffic. The investigation extends to the determination of overtopping levels for individual bridges, complemented by the establishment of vertical clearance thresholds spanning a range from 3 to 12.6 meters. Additionally, the research investigates the interplay between OW (%) and AADTT, revealing a direct relationship wherein heightened truck traffic contributes to an elevated percentage of excessive loading on bridges. Consequently, this heightened loading exacerbates the risk of bridge failures. The results of the analysis show for five different levels of flooding, a probability of failure ranging between 0.0155 and 0.0081. Bridges close to the Red River are at higher risk due to more water flow. The probability of failure due to overloading ranges from 3.74e-09 to 1.29e-08. The most vulnerable area in Manitoba is along the Trans-Canada highway and the north-south corridors near Winnipeg. This highlights the importance of considering specific locations when designing and maintaining bridges to ensure their safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".