SEISMIC PERFORMANCE OF OPERATIONAL AND FUNCTIONAL COMPONENTS (OFCS): FIELD OBSERVATIONS AND SHAKE TABLE TESTING
Bibliographic record
Abstract
Operational and Functional Components (OFC) are those elements in a building that are required for its normal function and operation. In recent earthquakes it has become clear that, in addition to the safety related aspects of the seismic performance of OFCs, the economic impact of the poor or marginal performance of them can be very severe. In this paper, a seismic risk assessment study conducted as part of a major project of the University of British Columbia called Joint Infrastructure Interdependencies Research Project (JIIRP) includes the evaluation of the performance of OFCs; a summary of the Seismic Risk Assessment considered for these components is presented first. The response spectra from the earthquake scenarios are used to compute floor response spectra (acceleration, velocity and displacement) in order to gain a better understanding of the demands experienced by OFCs. Secondly, a series of vibration tests were conducted on machinery and pipelines of actual buildings that are part of lifeline systems; the testing program included the evaluation of the dynamic properties of them using operational and forced vibration conditions. Then, a summary of a series of shake table tests of different types of OFCs conducted in recent years at the University of British Columbia is presented and the results are discussed. The results from field observations and laboratory tests are compared, and the similarities and differences between responses are discussed.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".