Advanced Reliability Analysis at Slope Crossings
Bibliographic record
Abstract
Abstract Pipelines cross diverse terrain and as a result are subjected to a variety of geotechnical hazards. Depending on the location of the pipeline relative to a geotechnical threat, it may be subjected to external forces which could lead to pipeline deformation or failure. Generally, geotechnical threats manifest as slope movement, subsidence/settlement, seismic waves, or frost heave/thaw settlement. While similar analysis techniques may have tangential applicability to all these threats, this paper focuses on the landslide/slope movement scenario. Here, the authors present an approach for evaluating pipelines in areas where slope movement is known or has the potential to occur. The methodology uses advanced finite element analysis (FEA) and statistical reliability techniques to estimate the probability of failure (PoF) of the pipeline at a given site. A case study where the method was employed is also presented. The presented process serves as an advanced analysis tool within a geohazard reliability program. This in-depth PoF analysis can be conducted after a screening level assessment has highlighted a given site. The data required for the analysis includes, at minimum: basic pipe properties, operational information, inertial measurement unit (IMU) in line inspection (ILI) pipeline centerline data, depth of cover survey data, and some estimation of relevant soil to pipe interaction parameters. Other information that can be incorporated to enhance accuracy and reduce conservatism include geotechnical reports and instrumentation measurements (e.g. slope inclinometers or strain gauges). The uncertainties associated with the inputs are estimated based on standards or subject matter expert (SME) input. Incorporating the defined uncertainties, numerical models are created using the commercially available finite element (FE) analysis software ABAQUS, where the pipe is modeled using pipe beam elements and the soil to pipe interactions is modeled using pipe-soil interaction elements. The FE models are processed using a design of experiments (DoE) approach to define response surfaces for both compressive and tensile strain demands. Strain capacities are estimated using the Dorey (U of A) and CRES (PRCI) models for compressive and tensile strains, respectively. Using the resulting relationships for strain demands and capacities, Monte Carlo simulations are completed using the previously defined uncertainties. The simulated cases where strain demand exceeds capacity produce an estimation of probability of exceedance (PoE). Finally, the PoF is obtained by multiplying the PoE by an estimated likelihood of slope movement occurring and impacting the pipe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".