Suitable engineering demand parameters for acceleration‐sensitive nonstructural components
Bibliographic record
Abstract
Abstract Early earthquake design codes used peak ground accelerations (PGAs) as intensity measures (IMs) to characterize the demands of ground motions on structures, but have since shifted towards using spectral accelerations because they provide a better indication of demand. The design of acceleration‐sensitive nonstructural components has followed a similar approach, with modern codes being based on an estimate of the spectral acceleration at the period of the nonstructural component. However, most fragility curves for loss assessment of acceleration‐sensitive nonstructural components, including the existing FEMA P58 library, continue to be based on peak floor accelerations (PFAs). Similar to PGAs as an IM for buildings, a limitation of PFA as an engineering demand parameter (EDP) for nonstructural components is its lack of dependence on the period of those components. In this study, fifteen alternative EDPs suggested in the literature are evaluated as potential candidates for developing seismic damage fragility curves. Acceleration‐sensitive nonstructural components are simulated by single‐degree‐of‐freedom (SDOF) components with elastic perfectly plastic behavior, with a period range of 0.01 to 1 s, and varying strength levels. Nonlinear response history analyses are conducted for the SDOFs, using floor motions obtained from both the first floor and the roof of buildings designed with four distinct seismic force‐resisting systems. Ductility demands for each SDOF are taken as an indicator of damage and are predicted using a linear regression model developed for each specific EDP. The suitability of candidate EDPs is evaluated based on their efficiency and relative sufficiency. Furthermore, a comparison is made between the expected annual loss calculated using fragility curves derived from the selected EDPs to quantify how the EDP used for a fragility curve can affect the seismic loss assessment. The results reveal that the PFA is a suitable EDP only for nonstructural components with very short periods (i.e., less than 0.1 s). Moreover, although the spectral acceleration at the period of the SDOF nonstructural component is a suitable EDP for components that are nearly elastic and are located on the roof of buildings, the peaks that develop in the floor spectra can grossly overstate the demands on nonstructural components that experience significant nonlinearity in their response. In such situations, an average of the spectral accelerations in a range of periods near the period of the SDOF nonstructural component is more appropriate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".