Failure probability minimization of buildings through passive friction dampers
Bibliographic record
Abstract
This paper proposes a methodology for robust optimization of the failure probability of buildings subjected to stochastic earthquakes, using a less common type of passive energy dissipation device: the friction dampers. There is a lack of studies on optimal positions and parameters of passive friction dampers, and additionally, the few studies found in the literature consider the problem in a deterministic way. The robust optimization proposed in this paper is carried out through the recently developed backtracking search optimization algorithm, which is able to deal with optimization problems involving mixed discrete (positions) and continuous (friction forces) design variables. In order to take into account uncertainties present in both the system and the dynamic excitation (earthquakes), some parameters are modeled as random variables, and consequently, the structural response becomes stochastic. For illustration purposes, a 10-story building is analyzed. The results showed that the proposed method was able to reduce the failure probability in approximately 99% with only three friction dampers, installed in their best positions and with their optimized friction forces. The proposed methodology is quite general, and it is believed that it can be recommended as an effective tool for optimum design of friction dampers. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".