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Record W2377771666

Reliability-Based Design and Maintenance Strategy for Composite Bridges

2012· article· en· W2377771666 on OpenAlexaboutno aff
Chan Yui-bun

Bibliographic record

VenueJianzhu gangjiegou jinzhan · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsGirderReliability (semiconductor)Structural engineeringService lifeEngineeringLife-cycle cost analysisBridge (graph theory)SlabCorrosionDeckReliability engineeringPreventive maintenanceMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Currently,the design practice of highway bridges around the world are moving towards limit states design,a reliability-based design procedure.Canadian Highway Bridge Design Code(CHBDC)is the first design code entirely developed based on limit states design philosophy,including foundations and FRP components.However,reliability of a structure decreases in service due to environmental attacks and material deterioration such as fatigue,corrosion and many other reasons.Therefore,the structure should be inspected periodically,and the reliability of the structure should be evaluated according to its age and field data.If its reliability is reduced to a certain level,a repair should be scheduled as well as some preventive maintenance measures should be implemented to prevent further deterioration.Recently,many research works have been conducted to investigate reliability-deterioration mechanisms for each type of infrastructure and its components,optimize the inspection and maintenance strategy,predict remaining service life,estimate its life cycle cost.This paper is focused on the study of reliability-deterioration mechanisms of slab on steel girder bridges due to fatigue and corrosion of steel girders as well as corrosion of reinforcement in the deck slab.Examples will also be given to illustrate the proposed life cycle management strategy for composite slab on steel girder bridges.

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 score0.561
Threshold uncertainty score0.883

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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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