Functional recovery evaluation of hybrid self‐centering piston‐based braced frames
Bibliographic record
Abstract
Abstract Severe earthquakes possess formidable destructive potential, leading to substantial damage, widespread disruptions, and tragic loss of life. In response, researchers and policymakers strive to develop innovative solutions to quantify the seismic resilience of structures. This study aims to evaluate the post‐earthquake functional recovery of two types of braced frames: Self‐Centering Piston‐Based Braced Frames (SC‐PBBFs) and Buckling Restrained Braced Frames (BRBFs). The SC‐PBBFs are designed to mitigate structural damage by incorporating Self‐Centering Piston‐Based Bracings (SC‐PBBs) equipped with Shape Memory Alloy (SMA) bars and Friction Springs (FS). A set of building prototypes, representing low‐, mid‐, and high‐rise archetypes in seismic regions, are subjected to comprehensive analyses under 44 far‐field ground motions. This thorough examination incorporates seismic hazards, structural demands, and component damages to quantify the earthquake‐induced losses and repair timelines. The study findings indicate that BRBFs suffer more significant collapse losses than SC‐PBBFs. However, BRBF systems incur lower repair costs, mainly due to limited damage to acceleration‐sensitive non‐structural components. Additionally, the FEMA P‐58/ATC‐138 framework is employed to estimate re‐occupancy, functional recovery, and full repair time, identifying systems and components that exert a significant impact on functional recovery.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".