Clustering‐based adaptive ground motion selection algorithm for efficient estimation of structural fragilities
Bibliographic record
Abstract
Abstract To accurately predict the seismic demands of structural systems, a proper set of ground motions representing the seismic hazard of a given site is needed. In general, such a set includes a large number of ground motions, and thus may result in high computational cost. To address this computational challenge without compromising the accuracy of structural fragility, this paper proposes a clustering‐based algorithm that can select a representative subset of ground motions adaptively from a given set of ground motions. First, critical features of ground motions that significantly affect seismic demands of the structural system are identified by Lasso regression of seismic responses of various single degree of freedom systems on existing intensity measures of ground motions. Second, ground motions are selected adaptively based on the hierarchical clustering of the critical features until the fragility curve converges. Applications to a reinforced concrete building and steel moment‐resisting frames demonstrate the improved efficiency and wide applicability of the proposed method. The results of the numerical examples confirm the robust performance of the proposed algorithm against various ground motions, structural types, and definitions of the limit‐states. The proposed algorithm enables us to obtain structural fragilities using a significantly reduced number of ground motions while keeping consistency with the available ground motion set.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".