Harmonizing structural safety levels with life-quality objectives
Bibliographic record
Abstract
Life-quality objectives are identified as an essential element in design decision making. Of particular concern is the question of optimal safety levels that are consistent with reasonable expectations of individuals in a present-day society. Using sound principles of decision analysis and utility theory, a lifetime utility function is developed. It is shown to be related to human consumption, life duration including the cumulative effects of mortality and discounting, and the relative amount of time spent on work versus leisure. Questions regarding the acceptability and affordability of changes in life quality can be addressed using the utility functions developed. As an application, design safety levels for the Confederation Bridge are examined and discussed. Life-quality objectives can also be included in a life-cycle cost optimization. This allows us to perform a level IV probabilistic design approach including costs and consequences without having to estimate the value of human life, but instead including the effect of consequences on changes in life quality of individuals at risk. This results in a useful tool to determine optimal limit state design safety levels, as is illustrated in a parametric analysis in the case of a single limit state.Key words: lifetime utility, life-quality index, risk acceptance, limit states design, target reliability levels, risk reduction, minimum life-cycle cost, structural safety.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".