Performance‐based optimum seismic design of self‐centering steel moment frames with SMA‐based connections
Bibliographic record
Abstract
Abstract Shape memory alloys (SMAs) have found several applications in earthquake‐resilient structures. However, because of high material costs, their implementation on industry projects is still limited. Developing design approaches that minimize the use of expensive SMAs is critical to facilitating their widespread adoption in real structures. This paper proposes a performance‐based seismic design optimization procedure for self‐centering steel moment‐resisting frames (SC‐MRFs) with SMA‐bolted endplate connections. The topology optimization uses a metaheuristic algorithm to minimize the frame's total cost, including the initial construction and expected repair costs. The design variables are the steel beam and column sections, SMA connection properties, and the topology of the SMA connections. Different constraints are considered, such as the constructability of the chosen steel sections, member strengths, performance‐based design, Park‐Ang damage index, and strong‐column weak‐beam requirements. Furthermore, the seismic safety of optimal designs is assessed by calculating adjusted collapse margin ratios according to FEMA‐P695. An illustrative optimization study using three‐ and nine‐story SC‐MRFs is presented. The optimal SC‐MRFs are then assessed in terms of cost and seismic performance. The results confirm the effectiveness of the proposed optimum design, which minimizes the use of SMAs while ensuring improved seismic performance. The case studies show that the optimal placement of SMA connections can reduce the total cost by up to 71% and 61% for the three‐ and nine‐story SC‐MRFs, respectively, compared to nonoptimal frames. Moreover, the optimal SC‐MRFs exhibit more uniform drift distributions, lower residual story drifts by up to 96%, and increase collapse capacity by up to 102%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".