Large-Scale Seismic Soil-Structure Interaction Simulation of Civil Infrastructures
Bibliographic record
Abstract
Developing detailed finite element models considering site terrain, accurate soil profile, structural details, and soil-structure interaction (SSI) is crucial for designing earthquake-resilient civil infrastructures. However, practical projects often overlook these factors due to their complexity and high computational demand. Therefore, four frameworks were created to address these challenges, thus allowing for the creation of large-scale infrastructure models with high fidelity.The soil-foundation-bridge interaction framework provides a practical tool for modelling complex soil-bridge systems. It incorporates borehole log data, simulates various foundation types, represents accurate embankment and abutment geometries, and reduces computational resources. The framework accurately modelled the Meloland Road Overpass bridge, captured inelastic soil-bridge responses, and matched measured acceleration time histories. The site-city interaction (SCI) framework overcomes challenges in modelling site-city models by accounting for site terrain and soil lithology from boreholes and buildings' arrangement and geometry. A pilot study of a city block in downtown Toronto revealed that buildings reduced the free-field surface ground motion by 15%. The study emphasized the importance of considering the actual geometry of buildings for accurate structural responses. An extensive parametric study further illustrated the SCI effect on the response of the site and city under various conditions. Accurately replicating the seismic response of critical components in large-scale numerical models requires component- and system-level decomposition techniques due to limitations in analyzing these models with a single tool. The integrated site-city interaction framework integrates a detailed target building model with a macro-scale site-city model using system-level decomposition. Dynamic analysis is conducted on a supercomputer for the large-scale site-city model, while the target building model is analyzed on a desktop computer. A pilot study investigating the effect of SCI on a residential city block showed minimal impact on the site and dynamic response of buildings when the target building was incorporated into the model. The soil-tunnel interaction framework employs component-level decomposition through multi-platform simulation to accurately capture the complex response of soil and tunnel systems. The framework successfully replicated the failure of the Daikai tunnel during the 1995 Kobe earthquake, highlighting the significance of capturing shear-related failure mechanisms of the center column on the tunnel failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".