Soil–Structure Interaction Effects on Seismic Responses of a Conventional and Isolated Bridge Subjected to Moderate Near-Fault and Far-Field Records
Bibliographic record
Abstract
Seismic isolation is a powerful tool for mitigating seismic risk and improving structural performance. However, some parameters, such as earthquake inputs and soil characteristics, influence the technology’s performance. This research aims to investigate the effects of soil–structure interaction (SSI) with regard to different moderate earthquakes associated with different distances of the source to the site, frequency content, and different soil characteristics on the seismic response of the isolated bridges. Near-fault (NF) and far-field (FF) records are applied to the conventional and isolated bridge with and without considering the underlying soil. For this reason, using the direct and simplified methods, three soil properties representing rock, dense, and stiff soils are modeled in Abaqus software. Nonlinear time history analysis (NLTHA) is carried out, and structural responses of both approaches in terms of maximum deck acceleration, base shear, and displacement of the deck and the isolation system are studied. Results demonstrate that the difference between the two approaches is significant. Using the simplified method is a rather simple approach that roughly captures the important features of the record characteristics and SSI. Furthermore, careful attention should be paid to the base shear responses and the isolator displacement demands, as they are significantly amplified in softer soils. In addition, the peak ground acceleration to peak ground velocity ratio (PGA/PGV) plays a decisive role in all dynamic responses. Records with a lower PGA/PGV ratio cause higher dynamic responses in terms of displacement and acceleration/force, regardless of the distance of the ruptured fault, while NF records show higher dynamic responses compared to FF records.
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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".