Evolutionary Aseismic Design And Retrofit Of Buildings
Bibliographic record
Abstract
Over the past decade, passive energy dissipation systems have provided an increasingly attractive approach for the seismic retrofit of existing structures, as well as, for the design of new seismically resistant structures. Many different types of passive devices have been developed and general design guidelines have been prepared. However, the choice between the device types for a specific application often is not clear, particularly when consideration must be given to the performance of non-structural components. For example, in general, are rate-independent devices and rate-dependent devices equally beneficial, or are there circumstances in which one of these two categories is preferable? Furthermore, regardless of device type selection, the designer also is faced with the complex issue of effective device distribution. In this paper, we present a genetic algorithm based methodology to address these aspects of aseismic design within the context of steel frame buildings. The primary structure is represented in terms of a nonlinear two-surface plasticity lumped parameter model. Meanwhile, the available passive device types include rate-independent metallic plate dampers, along with rate-dependent viscous fluid dampers and solid viscoelastic dampers. In order to capture more accurately the dynamic response, these devices are also represented by nonlinear models. The seismic environment is characterized either in terms of a fixed set of specified ground motions or by utilizing synthetic signals generated from geophysical models that simulate the actual uncertain seismicity of the site. Within the overall algorithm, passively damped structural designs evolve toward configurations that satisfy constraints on inter-story drift and absolute acceleration, while attempting to limit damper cost. For adjacent buildings, a separation constraint also may be included to alleviate structural pounding. Besides providing an overview of the simulation algorithm, the paper includes a number of illustrative examples to highlight the benefits of the proposed computational design approach. 1 Research Assistant, Dept. of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-2114, FAX 716/645-3733, sdogruel@buffalo.edu 2 Professor, Dept. of Mechanical and Aerospace Engineering, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-2593, FAX 716/645-3875, gdargush@eng.buffalo.edu 3 Research Assistant Professor and Computational Scientist, Center for Computational Research, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-6500, FAX 716/6456505, mlgreen@buffalo.edu June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".