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The Effect of Girder Profiles on the Probability of Fatigue Damage in Continuous I-Multigirder Steel Bridges

2025· article· en· W4409326775 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInfrastructures · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGirderStructural engineeringMaterials scienceForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Fatigue is one of the main sources of mechanical failure in steel bridges. However, a few studies have investigated the relationship between the longitudinal shape of bridge girders and long-term fatigue effects. This paper shows how different girder profiles affect the probability of fatigue damage occurring in continuous I-multigirder steel bridges. The analysis was conducted using realistic traffic scenarios defined through truck data collected in USA and Canada. Monte Carlo simulations with 5000 realizations were performed on several continuous bridge configurations with different span lengths and different girder profiles. The results of the analysis showed that the probability of fatigue damage is affected by profile shape and the smoothness of the transition between the maximum and minimum height of the cross section. In particular, the probability of fatigue damage on continuous I-multigirder steel bridges can be reduced by up to 26% for typical fatigue construction details over a bridge service life of 75 years by modifying the geometry of the girders during the design phase of the bridge.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.246
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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