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Record W2974094456 · doi:10.1139/cjce-2018-0674

A service life model of metal ties embedded in the mortar joints of brick veneer walls with applications to reinforced concrete

2019· article· en· W2974094456 on OpenAlexafffundvenueabout
Mark Hagel, Gary R. Sturgeon, Carlos Cruz-Noguez

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of AlbertaCanadian Concrete Masonry Producers AssociationCanadian Natural Resources
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsService lifeMortarCorrosionBrick and mortarGalvanizationService (business)Materials scienceStructural engineeringBrickEngineeringForensic engineeringMetallurgyComposite materialComputer scienceLayer (electronics)Business

Abstract

fetched live from OpenAlex

The issue regarding the corrosion of steel ties connecting brick veneer to a structural backing is well documented. In this paper, the predicted corrosion rate and resulting service life estimates, produced by a time-stepped service life model developed in Visual Basic for EXCEL, for metal ties embedded in mortar, are compared with the empirically determined corrosion rates and service lives of 16 zinc galvanized tie specimens taken from 13 buildings located in 6 different Canadian cities. This tie service life model, coined the “Tie Service Life Predictor”, correlates the external environment of the building to the tie life. As with most corrosion models for steel embedded in concrete (or mortar), the model is broken into two distinct phases: corrosion initiation using Fick’s law of diffusion and corrosion propagation. By considering the mortar surrounding the tie as the tie’s atmosphere, the Tie Service Life Predictor characterizes the microenvironment (atmospheric conditions) surrounding the tie from the macroenvironment conditions. Once the atmospheric conditions were established, the ISOCORRAG atmospheric corrosion model could be used to predict the corrosion rate of zinc galvanized steel ties embedded in the mortar joints of the exterior wythe of brick veneer wall systems. The methods used to create the Tie Service Life Predictor could also be applied to service life estimation of reinforcing steel in concrete structures such as bridge decks and parking structures.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.190
Teacher spread0.178 · 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

Citations6
Published2019
Admission routes4
Has abstractyes

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