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Record W4399907934 · doi:10.1139/cjce-2024-0081

Regional bridge risk assessment due to the combined effect of flooding and overloading events in Manitoba

2024· article· en· W4399907934 on OpenAlexafffundvenueabout
Graziano Fiorillo, Shadi Safa

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlooding (psychology)Bridge (graph theory)Forensic engineeringRisk assessmentEnvironmental scienceEngineeringRisk analysis (engineering)Civil engineeringComputer scienceBusinessPsychologyMedicineComputer security

Abstract

fetched live from OpenAlex

The challenges experienced by Canada’s aging infrastructure require probabilistic methods to address the threats that bridge systems are exposed to. This study provides a framework to evaluate risk associated with two major bridge hazards such as flooding and overloading in Manitoba. The probability of failure of highway bridges due to overloading is assessed as a function of traffic volume and overweight percentages. The probability of failure due to flooding is obtained from a spatial analysis of the water levels. Finally, consequences of failure are established from the analysis of insurance exposure, including climate change effects. Historic data are projected ten years into the future through an ARIMA model. The results of the analysis show that the risk for bridges in the south-west regions of Manitoba is expected to increase by 2035 up to 40% for water levels of 3.0 m and up to 0.6% for a threshold of 12.0 m.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.200
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes4
Has abstractyes

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