Providing Safety: Using Probabilistic or Deterministic Methods
Bibliographic record
Abstract
The primary objective of a good engineering design or maintenance process is to provide safety with optimized resources. Most parameters and models used in engineering have uncertainty — some more so than others. Probabilistic assessments strive to account for these uncertainties explicitly while the deterministic methods account for uncertainties implicitly by using conservative inputs and safety factors. Deterministic methods are preferred by many due to their simplicity. However if inputs and safety factors are not defined prudently with explicit consideration for uncertainties and consequences they can lead to unsafe or unduly conservative solutions. The main objective in using reliability based methodologies is to provide consistent safety by explicitly accounting for uncertainties in a probabilistically quantified manner. Reliability methods also allow the articulation of the level of safety. This level of consistency in safety cannot be achieved in a deterministic analysis using safety factors because uncertainties are not accounted for explicitly and consequently the uncertainties lead to variable solutions. However safety factors can be calibrated using reliability methods so that more consistent safety levels can be assured when using deterministic methods. There is a relationship between the reliability level and the deterministic safety factors. This relationship between reliability levels and deterministic safety factors is examined both from a mathematical and practical perspective. Consequently it is shown that reliability based methods can be used to calibrate deterministic methods to improve the consistency of the safety level with due consideration to underlying uncertainties and consequences. This kind of calibration is used in other industries such as structural design and nuclear facilities. Providing more consistent safety enables optimization of maintenance activities which enables the safest system to be provided with available resources. Currently the pipeline industry uses deterministic methods with conservative inputs that are not based on risk or safety principles. Consequently there is a large variation in the inputs and safety factors used in the industry. Some examples of these are safety factors used in response to inline inspection that vary from the reciprocal of the design factor to 1.1 for all location classes. This paper shows that the maximum safety factor achievable for a given design is defined by the original design factor and the ratio between flow stress and yield strength. It also shows the inadequacy of using safety factors that are not risk based. The paper focuses on the importance of using a sound risk based rationale for appropriate safety factors in deterministic methods. A glossary of terms is provided at the end of the introduction.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".