Simplified non-linear FE model to predict staged capacity deterioration of RC columns subjected to combined ultimate or seismic and reinforcement corrosion loads
Bibliographic record
Abstract
The quasi-static and the low frequency cyclic loading-to-failure tests have shown that the stiffness degradation and strength of RC columns are reduced in a staged pattern. Although several studies have demonstrated this strength and stiffness degradation using complicated high-level 2D and 3D modeling approaches, a simplified numerical approach is required. Simplified non-linear finite element model developed by the authors is used in this paper to capture the staged damage and collapse of RC columns subjected to extreme loads. The model performs inelastic sectional and element analysis phases considering the instantaneous and successive changes of the sectional and element properties throughout the progress of the loading steps and the critical stages of corrosion-induced damages. The model considers all possible state of strain distributions on a concrete beam-column, and the nonlinear instantaneous sectional properties and the internal forces are calculated ensuring the equilibrium of all the internal and external forces in the section level. It is found that the proposed simplified nonlinear finite element model is numerically stable in all cases of strain distributions, both in static and dynamic analysis. The model shows that the load displacement curve of the columns subjected to quasi-static loading or the envelop of the load-displacement hysteretic relationship can be found using quasi-static analysis. The model can predict the nonlinear behavior of non-corroded and corroded specimens with high accuracy. Further research is required to accurately define the critical stages of damages incorporating field and experimental data of damaged RC columns and higher level non-linear finite element techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".