Failure probability minimization of buildings through passive friction dampers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it