Risk‐based optimization of concentrically braced tall timber buildings: Derivative free optimization algorithm
Bibliographic record
Abstract
Abstract Mass timber materials are attractive alternatives for tall‐timber buildings (TBs), where the need for sustainability is apparent. Innovative structural systems and design methodologies are needed to fulfil performance requirements according to modern performance based approaches. This paper deals with the design and optimization of buckling restrained braces as earthquake protection system for tall‐TBs through risk‐based design procedure. This procedure controls the mean annual frequency of exceedance of several limit states evaluated through a SAC‐FEMA approach and using response spectrum linear analyses on linearized models for demand assessment. The features of the optimization procedure and the linearized models are shown through an application on a 20‐story mass‐TB located in a high seismic zone. The optimization is executed through a derivative‐free algorithm, the generalized pattern Search, adopting several solution strategies whose efficiency and effectiveness for this kind of applications are shown and discussed. Finally, the results are compared and validated through the execution of non‐linear analyses within a multiple stripe framework.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".