Combined effect of reinforcement corrosion and seismic loads on RC bridge columns: modelling
Bibliographic record
Abstract
Columns are often the most vulnerable elements in reinforced concrete (RC) bridges as their failure could lead to bridge collapse. Characteristically, columns are subjected to combined static and dynamic eccentric and lateral forces due to traffic, self-weight, and earthquake loads. For the assessment of bridge columns in seismic areas, it is essential to evaluate their state of damage, strength and deformation capacity over their service life. In this paper, a nonlinear elasto-plastic numerical model to simulate bridge columns under the combined effects of reinforcement corrosion and seismic excitation is presented. The study includes the development of a comprehensive and yet simple tool to evaluate the seismic performance, and the residual strength and deformation capacity of aging RC bridge columns suffering from reinforcement corrosion. The model enables evaluating aging and deteriorated RC bridge columns safety, the decline in their energy dissipation capability, and the level of earthquake excitation that they may survive. Hence, the model can represent a useful tool for bridge engineers to optimize the use of available resources and define the critical capacity of bridge columns against earthquake events. It is found that the model is efficient in simulating the behaviour of the column under corrosion and seismic loads. From the case study, it is found that the load carrying capacity of the corroded column is much lower than that of non-corroded columns. The results show significant reduction in the column displacement capacity and energy dissipation capability due to rebar corrosion. The corrosion-induced damage could result in accelerated degradation of the bridge columns and reduce its ultimate strength.
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 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) | 0.000 | 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) | 0.000 | 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".