Estimating Landslide Induced Probability of Failure to Pipelines Using a Structured Reductionist Approach
Bibliographic record
Abstract
Much of North America, and indeed much of the global landscape, is comprised of either locally or regionally steep slopes, river valleys, and weak or unstable geology. Landslides and ground movements continue to impact pipelines that traverse these regions. Pipeline integrity management programs (IMP’s) are increasingly expecting quantitative estimates of ground movement or pipe failure as part of pipeline risk management systems. Quantitative analysis usually relies on one or more of statistics, physical models, and expert judgment. Statistics incorporate ground and pipe behavior (for hazard and vulnerability respectively) over a broad area to infer local probabilities. They carry the weight of big data, but the local application is almost certainly incorrect (variability even for regions exceeds 2 orders of magnitude). Detailed geotechnical (hazard) and soil-pipe interaction and stress (vulnerability) models provide rigorous results, but require substantial effort and/or expert judgment to parameterize the inputs and boundary conditions. We present herein a structured tool to calculate probability of failure (PoF) using expert judgment supported by known, instrumented or observable conditions and statistics (where available). We provide a series of tables used as a basis for nodal calculations along a branch path of a decision tree, and discuss the challenges and results from actual application to over 100 sites in the Interior Plains. The method is intended to be a practical informative approach based on, and limited by, data inputs. It is a flexible fit for purpose assessment that takes advantage of the best available data, however, the method relies on the user to articulate a level of confidence in, or the basis of the results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".