An Innovative Simulation-Free Approach for Probabilistic Assessment of Buried Pipeline Integrity Under Landslide-Induced Ground Movement
Bibliographic record
Abstract
Abstract Buried pipelines serve as reliable conduits for transporting energy products such as oil, gas, and chemical fluids. Their pivotal role in facilitating the transportation of these resources underscores their substantial impact on businesses, economies, and the overall quality of life for people. Landslide-induced ground movement poses a risk to buried pipelines, potentially causing buckling damage or tensile rupture, threatening their structural integrity and operational safety. Hence, it is crucial to investigate the response of buried pipelines to mitigate the potential impacts of landslide-induced ground movement. However, due to the uncertainties in material, geometry, soil properties, and ground movement, it is imperative to conduct reliability-based analyses rather than deterministic analyses. Reliability methods like Monte Carlo, known for their simplicity and effectiveness, are widely used in reliability analyses. However, their significant drawback lies in the need for extensive simulations to generate responses, a challenge that proves impractical for studying pipelines buried through areas prone to ground movement. As such, this study introduces an innovative simulation-free approach based on the Physics-Informed Neural Network (PINN) to predict the response of inelastic pipelines under landslide-induced ground movement. Using the simulation-free approach for response predictions, the Monte Carlo simulation is then employed to determine the probability of failure. PINN offers a comprehensive solution to the underlying physics of the problem by incorporating stochastic variables like ground movement and eliminating the need for an extensive number of simulations to obtain the response. PINN utilizes a deep learning approach that operates without the need for training data by leveraging the underlying physics expressed through differential equations. The applicability of the employed method to pipelines subjected to permanent ground movement is demonstrated through a case study. The strain-based limit state function relies on established equations from the literature for determining strain capacity, while the strain demand is predicted using the PINN-based approach.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".