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Record W4315874477 · doi:10.1680/jbren.21.00051

Enhancing the capacity evaluation of Canadian bridges with structural monitoring data

2023· article· en· W4315874477 on OpenAlexaffabout
Aftab A. Mufti, Baidar Bakht, Andrew Horosko

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Bridge Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBridge (graph theory)Reliability (semiconductor)Reliability engineeringIndex (typography)Computer scienceEngineering

Abstract

fetched live from OpenAlex

The Canadian highway bridge design code (CHBDC) uses the concept of a target reliability index for evaluating the load-carrying capacity of existing bridges. This index, which is based on risk to human life, is related to three aspects of uncertainties inherent in a bridge: element behaviour, system behaviour and inspection level. Analysis is yet another uncertainty in bridge evaluation. It is assumed that all bridge inspections are manual. Citing examples of tests on many instrumented bridges, another level of inspection is proposed, carried out with the help of electronic instruments and tests under controlled vehicle loads. Simple additions to the clauses of the CHBDC are proposed, which can be used to determine the optimum load-carrying capacities of existing bridges where structural monitoring information is available.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.033
GPT teacher head0.230
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes2
Has abstractyes

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