Modeling the effect of corrosion on bond strength at the steel–concrete interface with finite-element analysis
Bibliographic record
Abstract
This paper examines the basic influence of corrosion on bond strength at the steel–concrete interface and the associated slip and cracking. A nonlinear finite-element model is developed to account for the effect of corrosion on deterioration of the bond. Deterioration of the mechanical interaction between the corroding reinforcing steel and the concrete is modeled with the nonlinear finite-element program ABAQUS. The contact pressure normal to the steel–concrete interface is reduced when the concrete cracks, which occurs along with a decrease in the cross-sectional area of the steel bar and the decrease of the friction coefficient between the steel and the concrete. The loss of contact pressure and the decrease in the friction coefficient with the mass loss of steel bars are evaluated using pullout test specimens with different levels of rebar corrosion. Finally, the relationship between the loss of bond strength and the mass loss of the steel rebar is established. The model gives reasonably accurate predictions of bond strength for three independent experimental studies. Key words: bond, concrete, corrosion, mechanical interaction, reinforcing steels, slip, steel-concrete interface.
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 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 | 0.001 |
| 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".