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Record W2014382796 · doi:10.1061/41170(400)53

A Polynomial Chaos Approach for Uncertainty Analysis of Chloride-Induced Corrosion in Concrete Structures

2011· article· en· W2014382796 on OpenAlexaff
Bassem Saassouh, Zoubir Lounis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsServiceability (structure)CorrosionDurabilityService lifeChloridePolynomial chaosProbabilistic logicStructural engineeringConcrete coverReinforced concreteMonte Carlo methodMaterials scienceComputer scienceEnvironmental scienceEngineeringComposite materialMetallurgyMathematics

Abstract

fetched live from OpenAlex

Chloride-induced corrosion has been identified as one of the main causes of deterioration of concrete structures, such as highway bridges, etc. The development of a performance-based approach is critical to ensure adequate safety, serviceability and durability of concrete structures, built in chloride-laden environments, as well as to help identify appropriate maintenance strategies to extend their service life. This paper presents a polynomial chaos response surface approach for the probabilistic modeling of chloride-induced corrosion of carbon steel reinforcement in concrete structures that takes into account the uncertainties in the parameters that govern the physical models of chloride ingress into concrete and corrosion of carbon steel, including concrete diffusivity, concrete cover depth, surface chloride concentration and threshold chloride level for onset of corrosion. A case study of highway bridge deck was used to illustrate the applicability, accuracy and computational efficiency when compared to crude Monte Carlo simulation.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.040
GPT teacher head0.241
Teacher spread0.201 · 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 designBench or experimental
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
Published2011
Admission routes1
Has abstractyes

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