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Record W3196921556 · doi:10.1177/03611981211036678

Comparison of International Brittle Fracture Design Code Provisions for Steel Bridges

2021· article· en· W3196921556 on OpenAlexaffabout
Michelle Y. X. Chien, Scott Walbridge, Bertram Kühn

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2021
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsForensic engineeringBridge (graph theory)EngineeringBrittlenessBrittle fractureFracture (geology)Fracture toughnessGeotechnical engineeringMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Brittle fracture is a major concern to structural engineers as it has significant consequences for safety and cost. Although modern day occurrences of brittle fracture are rare, it is well known that they can occur without warning and may lead to the sudden closure of a bridge, loss of service, expensive repairs, and/or loss of property or life. In Canada, steel bridge fracture is a particularly significant concern because of the harsh climate. If the toughness properties are improperly specified, many steels could be on the lower shelf of the toughness-temperature curve. A comparison of brittle fracture design provisions around the world reveals that more sophisticated approaches have been developed for modeling and understanding brittle fracture in existing and new bridges than those currently in use in North America, including Canada and the U.S.A. This paper describes the European brittle fracture provisions and presents a comparison of the North American and European design provisions using the example of a typical steel-concrete composite highway bridge. On the basis of this comparison, situations where one set of design rules may be more or less conservative are identified, and opportunities for improvement and areas warranting further study are highlighted.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.141
GPT teacher head0.415
Teacher spread0.274 · 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 teacher head, 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

Citations4
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicFatigue and fracture mechanicsFrench-language works237,207