Reliability-based simultaneous optimization of quantities, positions, and parameters of inerter-based vibration absorbers to minimize the probability of building failure during earthquakes via the CIOA
Bibliographic record
Abstract
This study introduces a novel approach for the reliability-based optimal design of inerter-based dynamic vibration absorbers (IDVAs), such as tuned inerter dampers (TIDs) and tuned mass damper inerters (TMDIs) to be implemented in buildings located in seismic areas. Additionally, the proposed approach is also applied to conventional tuned mass dampers (TMDs) for comparison purposes. Rather than focusing solely on optimizing the parameters of IDVAs and overlooking uncertainties through deterministic optimizations, the approach proposed here seeks to minimize the risk of building failure, using fragility curves, by optimizing not only the device parameters but also the quantities and positions of IDVAs, considering the inherent uncertainties associated with seismic excitation. To achieve this, the Circle-Inspired Optimization Algorithm (CIOA), a modern optimization technique developed by the authors, is implemented. Actual seismic accelerogram data from the target site are utilized to assess seismic risk, determining the expected annual exceedance rate of peak ground acceleration (PGA). Probability distribution functions of PGA for one and 50-year periods are fitted to estimate seismic risk, which is then evaluated through numerical simulations to ascertain building reliability. To illustrate the proposed methodology, it is studied a building located in Cúcuta, Colombia, where earthquake data are available. Robust estimators of system reliability are computed using a diverse seismic records database. The findings demonstrate that the proposed simultaneous optimization methodology significantly enhances the reliability of the building, underscoring the effectiveness of IDVAs in reducing the probability of building failure in regions with substantial seismic activity, and also showing that solutions with TMDIs or TIDs are more advantageous than those with TMDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".