A Probabilistic Method for Assessing Pipeline Strain Demand Using InSAR Ground Movement Data
Bibliographic record
Abstract
Abstract Ground movement hazards, such as landslides, ground settlement, and heave, pose a major threat to buried pipelines and resulted in $391M USD in property damages in the United States from 2002 to 2021. The dynamic nature of ground movement makes it necessary to actively model and predict pipeline integrity in order to maintain a reliable pipeline network. Strain-based design and assessment (SBDA) methods excel at predicting pipeline failure in the presence of large longitudinal strains that commonly result from ground movement hazards. Applying SBDA methods to operational pipelines requires the estimation of strain demand, the strain induced on the pipeline. Synthetic Aperture Radar (SAR) data, acquired by satellite, can be used to compute ground movement using SAR interferometry (InSAR). When combined with pipe-soil interaction (PSI) modeling, InSAR offers an attractive method for estimating strain demand; InSAR can cover large areas of interest and provide precise ground displacement measurements at a high spatial and temporal resolution. This paper presents a probabilistic method for predicting pipeline strain demand using ground movement data computed with InSAR. Pipe-soil interaction models from prior research were integrated into a Bayesian network (BN) which accounts for the environmental effects on pipeline displacement. Model performance was tested using a landslide case study in which the predicted axial strain was within reason. However, the model needs further work to accurately predict bending strain. The high resolution of InSAR data and the use of BN models enable the probabilistic evaluation of strain demand without the need for finite element method (FEM) models. The proposed method can empower pipeline companies to perform pipeline integrity assessments with greater ease, promoting a fast and data-informed response to ground movement hazards.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".