Probabilistic Methods in Engineering: Quantifying Uncertainty and Implementing Performance-Based Design
Why this work is in the frame
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Bibliographic record
Abstract
The last decades have seen great advances in computational models and in the ability to tackle previously intractable problems in mechanics. Yet, these models normally incorporate many variables which are not deterministic, introducing uncertainty in the calculated output. The problem of estimating the probability distribution of the output, given the probabilistic description of the input, has als o received much at tenti on since the 1 9 6 0 âs and many computational techniques have now been developed to account for the probabilistic nature of the problem. This paper discusses the integration of analysis and reliability assessment, including examples of large Canadian projects and design codification. Current objectives in probabilistic methods include the development of performance-based design. This paper discusses this topic in the context of earthquake engineering, with an application to the performance-based design of a steel pile under seismic excitation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it