Time Dependent Reliability Analysis for Oil and Gas Pipelines: A Bayesian Spectral Analysis-Based Deterioration Model
Bibliographic record
Abstract
Abstract Oil and gas pipelines are essential infrastructures that sustain the economy of modern society. They are designed for continuous and reliable operations over their service lives. Once installed, however, their reliability is affected by several threats among which external corrosion plays a significant role. Corrosion-based pit depth growth reduces the wall thickness over time that consequently affect the mechanical strength and the hydraulic performance of the pipeline. Pipeline utility managers rely on the corrosion growth rate models to plan their maintenance, rehabilitation and/or replacement. Existing pipeline deterioration models are mostly based on the power law function that relates the pit depth with the exposure time and rarely include the soil factors that can have effect on the corrosion growth rate. Moreover, the way these factors affect the corrosion rate is complex and cannot be captured with simple linear relationship. This paper uses data found in the literature to build a nonlinear pit depth growth model based on Bayesian spectral analysis regression technique. All continuous covariates are allowed to have smooth nonlinear spectral representations of their effect function on the pit depth growth. The discrete (i.e. categorical) factors are modeled using the ordinary least squared algorithm. The final semiparametric model allows to capture all pit depth measurements, even those difficult to be modeled using high degree polynomials. The stochastic nature of the pit depth growth is captured through the Bayesian approach. A time dependent reliability analysis using subset simulation is carried out to evaluate the changes occurring in the probability of failure of the pipe over time and allow for a better planning and management of these important infrastructure. The model is applied on a bare pipe directly exposed to the soil environment over time. The Bayesian pit depth growth model is accurate enough to allow the computation of the time dependent reliability of pipelines considering both the mechanical and hydraulic reliabilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".