Finite Element Modelling of Corrosion Damaged Reinforced Concrete Structures
Bibliographic record
Abstract
Corrosion of steel is the predominant deterioration mechanism of reinforced concrete structures throughout the world. This thesis presents the work performed on VecTor2, a nonlinear finite element analysis program developed at the University of Toronto, for analysis of corrosion damaged reinforced concrete structures. Two well-known types of corrosion, namely uniform and pitting, were considered for modelling. Corrosion damage was incorporated in the algorithms of VecTor2 through reduction of the sectional area of reinforcing steel, the bond strength between the reinforcement and concrete, and the mechanical properties such as yield strength of a corroded reinforcing bar.\nThe employed techniques for incorporating corrosion damage in VecTor2 successfully reproduced the load-deflection response of published experiments. Stochastic simulation of the same experiments, performed by employing the stochastic tools of VecTor2, demonstrated the sensitivity of response quantities to changes in various input parameters forming the basis for further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".