Traditional approaches to uncertainty management in pipeline integrity often rely on conservative design factors that obscure the true variability of crack-related degradation mechanisms. This can lead to overly cautious interventions or undetected failure risks. This presentation introduces a probabilistic assessment framework specifically designed for evaluating crack-type anomalies, leveraging statistical modeling of large-scale in-line inspection (ILI) datasets to quantify exceedance probabilities with greater precision.
The methodology contrasts deterministic design factor approaches with Monte Carlo simulations that explicitly model uncertainty dispersion across defect geometry, material properties (e.g., fracture toughness), and inspection method performance (e.g., detection thresholds, sizing accuracy). Python-based routines are utilized to process extensive datasets, generate probabilistic distributions, and visualize risk profiles across both short- and long-term horizons. These tools enable engineers to quantify the likelihood of limit state exceedance, thereby enhancing traceability and defensibility in integrity evaluations.
A structured decision model is also presented, built on statistical analysis of multiple crack detection runs. This model supports asset integrity planning by integrating automated sampling, geospatial routines, and visualization workflows. The approach improves consistency across inspection cycles and enables scalable deployment across diverse pipeline systems.
By replacing rigid thresholds with data-driven insights, the framework supports more informed decisions, optimized maintenance strategies, and improved operational reliability—grounded in a defensible understanding of material and inspection uncertainty.