Optimal Seismic Design Considering Risk Attitude, Societal Tolerable Risk Level, and Life Quality Criterion
Bibliographic record
Abstract
The minimum expected life-cycle cost decision rule leads to an optimal design for a risk-neutral decision maker, but fails to incorporate the magnitude of uncertainty in the life-cycle cost and is incapable of coping with risk attitudes. The maximum expected utility decision rule results in a utility function dependent optimal decision that may not be accepted by a decision maker with a different utility function. This study develops a framework for selecting efficient or optimal seismic designs by considering the decision maker’s risk attitude, societal tolerable risk level, and societal life quality criterion. Analysis results suggest that use of the developed framework can identify different sets of optimal designs for different risk attitudes. The results also show that the societal life quality criterion, for the considered examples, could only lead to a lower bound on the seismic design level for risk-seeking decision makers, whereas a reasonable tolerable risk level could provide a lower bound on the seismic design level for risk-seeking or risk-averse decision makers, or deny any acceptable designs for risk-neutral and risk-seeking decision makers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".