Towards Solid Ground: Integrated Components of Landslide Threat Management
Bibliographic record
Abstract
Abstract As conduits of energy, pipelines convey products from a point of origin to end-users. On the way, each pipeline inevitably traverses natural geographical landforms, including slopes and waterways, which can impart stresses to the pipe. Over time, weather-related and outside forces alter the landscape and pipelines may be subject to interacting effects of ground movement, in terms of imparted high axial/longitudinal stresses that may not have been accounted for in the pipeline design. Evaluation of these effects can identify when the operating safety margin of the pipe is threatened and if intervention is required to maintain structural integrity. Different than the steady progression of time dependent corrosion and axial cracking, ground movement is episodic with a trend towards accelerating rates leading to slope failure. Successful management of ground movement therefore requires sufficient lead time to observe early effects and plan intervention. We present herein the integration of three components that form a basis for holistic landslide threat management: a structured tool that estimates probability of failure (POF) for landslides combined with a parameterized tool that evaluates pipe resilience underground movement, and a visual analytics tool for ILI/IMU bending strains, that independently validates the findings of POF and pipe resilience. The POF Calculator for Landslides is a structured tool to calculate probability of failure (POF) using expert judgment supported by known, instrumented or observable conditions and statistics (where available). This tool is augmented with discretized Pipe Vulnerability relative to magnitude of displacement for more accurate characterization of effects due to ground movement, thereby providing a high-level screening for strain-based design and assessment (SBDA) in advance of full evaluation using FEA. We show how analysis of measured bending strains can be used to corroborate findings from the POF Calculator for Landslides, providing assurance that the model is valid. The integration of these components is designed to provide a reliable basis for informing decisions and establishing plans to manage the threat of landslides interacting with pipelines.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".