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Record W1996370923 · doi:10.1139/l06-081

Assessment of precast stringer highway bridges using mean load method

2006· article· en· W1996370923 on OpenAlexfundvenueaboutno aff
Daman K. Panesar, F. Michael Bartlett

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringStringerPrecast concreteAxle loadEngineeringBending momentStructural loadBridge (graph theory)AxleGeotechnical engineering

Abstract

fetched live from OpenAlex

The mean load method of the Canadian Highway Bridge Design Code is used to evaluate the shear and bending moment reliability of existing precast "type G" stringer bridges in Alberta that date from the late 1950s. The overall stringer population is categorized into distinct subpopulations using bridge-specific factors, including the degree of deterioration and approach span condition, which are readily identified during a brief field visit or from inspection reports. Critical sections to be investigated for reliability resisting shear forces or bending moments are determined. The reliability indices decrease if the reinforcement is corroded or the bridge approach is not smooth, and the reduction of the maximum axle loads permitted by legislation due to these factors is quantified. For bridge subpopulations where the actual reliability index is less than the target value for current legal axle loads, the critical axle load for moment is less than that for shear. Therefore, if flexural distress is not noted during inspection of such structures, they are likely adequate for the actual loading they are subjected to.Key words: corrosion, deterioration, dynamic load allowance, mean load method, reliability, visual inspection.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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

Citations2
Published2006
Admission routes3
Has abstractyes

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