Simplified finite element model for evaluation of ultimate capacity of corrosion-damaged reinforced concrete beam-columns
Bibliographic record
Abstract
A simplified nonlinear finite element analysis (NLFEA) based on enhanced inspection, material testing, and nonlinear sectional analysis is introduced as part of a semi-quantitative assessment approach of aged beam-columns. The focus is on the evaluation of the structural performance and residual capacities of slab-on-girder bridge columns subjected to combined external loads and reinforcement corrosion. NLFEA takes into account different levels of geometrical, material, and bond damage due to reinforcement corrosion. At each load step of the nonlinear analysis process, NLFEA establishes the instantaneous stiffness of the structure through effective transfer of the instantaneous axial and flexural rigidities from the sectional level to the element level. The model adopts a displacement field tuning convergence approach that involves single or multiple correction phases satisfying the equilibrium and any user-defined displacement tolerance. The efficiency and accuracy of the proposed NLFEA is verified by comparison with test and analytical results from previous studies conducted on undamaged and corrosion-damaged structural elements. NLFEA proves to have high numerical stability and fast convergence, establishing its adoptability in large structural analysis/assessment frameworks. For corrosion-damaged beamcolumns, it is found that critical design sections do not necessarily remain critical for the structural evaluation.
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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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".