A service life model of metal ties embedded in the mortar joints of brick veneer walls with applications to reinforced concrete
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".