Numerical Study on Bar Slip Effects in Reinforced Concrete Bridge Bents Under Cyclic Loading
Bibliographic record
Abstract
Assessing the bridge behaviour against cyclic loading after some powerful earthquakes, i.e., 1995 in Kobe, 2010 in Maule, showed thatbridge structures shouldbe designed andconstructed withgood detailing. Technical investigations showed that old bridge bents, i.e., that were designed prior 1980s in North America, are not equipped with appropriate detailing, especially in the beam-column joint. The most important factor in maintaining the composite application of reinforced concrete elements is the bond between rebars and surrounding concrete. Rebar slips and bond deterioration in cap beam-column joints in bridge bents, play a significant role on the cyclic behaviour under seismic loads. Previous studies on bridge bents indicated that in old bridge bents, damages initiate in the joint area, as a result of insufficient anchorage length of column longitudinal bars and lack of transverse reinforcement around them. Many researchers proposed different retrofitting schemes, such as applying transverse pressure on the joint area. However, to rehabilitate structures, first their as-built behaviour must be assessed. The common approach in assessing responses of older structures (used in present study) is collecting design details of structures, then develop a numerical model to simulate the experimental models, and real scale structures. The purpose of this research study is to propose a method in macro modelling to simulate the cyclic behaviour of older bridge bents with joint detailing deficiencies. Moreover, the effect of joint rehabilitation by applying external pressure on joints is investigated. In this case a fibre element, named slip simulator element, is developed in Opensees software. This model will be calibrated with experimental results on ordinary 2-span highway bridge bents which carried out by other researchers. To achieve an accurate macro modelling in Opensees, the input specifications of the macro model is attained from a micro model developed in ABAQUS. Comparing analytical results of macro modelling with experimental evidence shows that generally the proposed analytical approach is effective in predicting the cyclic response of both existing and rehabilitated bridge bents. Moreover, the proposed micro numerical modelling to determine inputs for the macro model is a reliable approach and has a good agreement with experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".