Spatial Effects in Risk-Based Design and Maintenance of Pipelines
Bibliographic record
Abstract
Pipelines are to a large extent spatially continuous systems having a system-component relationship that is not as clearly articulated as for other structural systems. Reliability-based design methods for pipelines often provide conflicting views about the spatial extent of limit states, the effect of spatial correlation, the applicability of target risks and target reliabilities (for instance on a per unit length basis), the link with lifecycle cost methods, and risk acceptability in general. The present paper first reviews probabilistic design and assessment approaches for pipelines, ranging from partial factors and limit state design, to reliability based and consequence-based methods. Subsequently we identify the various types of limit states from the point of view of their spatial characteristics. The paper also reviews the possible approaches to target risks and target reliabilities in view of the different spatial extent of the limit states. The role of spatial correlation as it impacts on different kind of pipeline limit states and on the risk acceptance process is discussed. The role of inspection, repair and maintenance can easily be included in many of the reliability-based pipeline design and assessment approaches as the lifetime costs of mitigative actions are fairly well defined, together with the spatially distributed consequences of failure, but they do add some additional challenges to the spatial modeling of the system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".