Limit state design of resilient earthquake-resisting systems
Bibliographic record
Abstract
Sustainable seismic design (SSD) is the first step towards next-generation earthquake engineering. SSD of mixed multiple seismic systems (MMSSs), in which more than one earthquake-resisting structure is used, is a challenging structural engineering issue. Seismic sustainability implies survivability during and after an earthquake, preceded by post-earthquake realignment and repairs (PERR). Purpose-specific MMSSs are ideally suited for SSD. However, contemporary codes address neither the sequences nor the failure mechanisms of the earthquake-resisting structures of MMSSs. The difference between conventional design and SSD is their approach to expected behaviour during and after an earthquake. Earthquakes are natural and dynamic occurrences, whereas PERR is a manual and static process. SSD does not favour unreal detailing or oversimplifying assumptions; it is a multi-faceted effort that involves realistic structural analysis and planned manual operations. In this context, design implies operability with a view to PERR and requires a change from damageability assessment and performance-based seismic design to performance control and reparability-based design, including the development of new analytic tools and purpose-specific details. This article presents graphical solutions and theoretical principles to help achieve practical SSD for MMSSs. These new techniques may be used for practical reliability, economy and environmental protection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".