ASSESSMENT OF SEISMIC HAZARD AND RISK, AND DECISION-MAKING UNDER UNCERTAINTY
Bibliographic record
Abstract
A simulation-based approach to assess seismic hazard and risk is developed and implemented. Its use together with the seismic hazard model proposed by the Geological Survey of Canada is presented. Sensitivity analyses investigating the effects of smoothing historical seismicity, epistemic uncertainty, attenuation relations, and time-dependent earthquake occurrence on seismic hazard are given. By using the developed lifecycle cost model of a structure and adopting the minimum expected lifecycle cost criterion, the optimal seismic design level for Canadian sites is investigated. Note that the use of the adopted criterion identifies the optimal seismic design level for risk-neutral decision makers. Results suggest that cost-effective seismic design levels vary over geographical regions and the use of uniform reliability levels is suboptimal in terms of economic efficiency. To incorporate the effects of risk attitudes of decision makers of perfect rationality, the stochastic dominance criteria together with the societal tolerable risk and life quality index criteria are considered. The proposed framework identifies sets of acceptable and efficient seismic design levels for risk-averse and risk-seeking decision makers. Furthermore, the impacts of risk perception and cognitive limitations of decision makers of bounded rationality as well as available earthquake insurance options on the implied seismic design preference are assessed. Such an assessment is new in earthquake engineering and structural safety. Results suggest that possible ranges of preferred seismic design levels by decision makers with diverse risk attitudes are wide, and that the seismic design level based on the minimum expected lifecycle cost can be used as a yardstick to evaluate the effects due to limited cognitive capability and risk perception. Finally, the seismic risk model is extended to consider multiple buildings subjected to spatially correlated seismic excitations. This is a novel approach. Results suggest that the mean of aggregate seismic loss is not significantly affected by the treatment of the iii spatial correlation of seismic demand, whereas the standard deviation and fractiles of aggregate seismic loss are greatly affected. Therefore, adequate consideration of the correlation is important for assessing seismic exposure and vulnerability of spatially distributed buildings and for managing catastrophic seismic risk successfully.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".