Modeling the Bond Stress at Steel-Concrete Interface for Uncorroded and Corroded Reinforcing Steel
Bibliographic record
Abstract
Corrosion of reinforcing steel causes cracking and spalling of concrete structures, reduces the effective cross-sectional area of the reinforcing steel and the concrete simultaneously decreases the bond strength at the steel-concrete interface. The detrimental effect of corrosion on the service life of reinforced concrete structures highlights the need for modeling of bond strength between the corroded steel and the concrete. This research presents a nonlinear finite element model for the bond stress at the steel-concrete interface for both uncorroded and corroded reinforcing steel. The nonlinear finite element program ABAQUS is used for this purpose. The expanded volume of corroded product of reinforcing steel produces radial and hoop stresses which cause longitudinal cracks in the concrete. The increased longitudinal crack width, the loss of effective cross-sectional area of the steel and the concrete is also reduced due to the lubricating effect of flaky corroded layer. This research models the loss of contact pressure and the decrease of friction coefficient with the mass loss of the reinforcing steel. The model analyzes the pullout tests of Amleh (2002) and a good agreement is noted between the analytical and the experimental results. Both in FE analysis and experimental results, the loss of bond capacity is almost linear with mass loss of rebar. FE analysis and experiemental result show that, up to 5% mass loss, the bond capacity loss is moderate, at 10 to 15% mass loss, significant amount of bond capacity is lost and at about 20% mass almost all bond capacity is lost. The model is also validated by analyzing the pullout tests performed by Cabrera and Ghoddoussis (1992) and those by Al-Sulaimani et al.(1990).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".