Seismic analysis of the RC integral bridges using performance-based design approach including soil structure interaction
Bibliographic record
Abstract
Bridges in high seismic risk zones are designed and built to withstand damage when subjected to earthquakes. However, there have been cases of bridge collapse due to design flaws around the world in the last few decades. To avoid failure and minimize seismic risk, collapse issue should be appropriately addressed in the next generation bridge design codes. One of the important subjects that needs to be addressed in bridge design codes is Soil-Structure Interaction (SSI), especially when the supporting soil is soft. In this research, SSI is incorporated within a performance-based engineering framework to assess the behaviour of RC integral bridges. 3-D nonlinear models of three types of integral bridges with different skew angles are built. For each bridge type, two archetype models are constructed with and without considering the effect of SSI. CALTRANS spring and multi-purpose dynamic Winkler models are employed to simulate the effect of soil in the SSI simulation. In this study, relative displacement and drift of the abutment backwall and pier columns are considered as engineering demand parameters (EDPs). Spectral acceleration of ground motions is chosen as the intensity measure (IM). Incremental dynamic analysis (IDA) is employed to determine the engineering demand parameters and probability of collapse using a set of 20 well-selected ground motions. Current study shows that for the integral abutment bridges considering soil structure interaction mostly demonstrate smaller relative displacement capacity/demand ratio. Therefore, neglecting SSI can result in overestimating relative displacement capacity of the structural components in this type of bridges. In addition, it is shown that SSI can cause an increase in ductility of the pier columns while it can cause a decrease in the ductility of the abutments. Collapse Margin Ratio (CMR) is considered here as a primary parameter to characterize the collapse safety of the structures. It is found that the probability of collapse of the SSI archetype models is higher than probability of collapse of their corresponding non-SSI models. Consequently, CMR value of the SSI archetype model is smaller than CMR value of its corresponding non-SSI models.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".