Reliability Analysis of Slow Crack Growth in HDPE Pipes: Impact of Buried Pipeline Design and Soil Characteristics

Abstract

This study delves into one of the most common fracture mechanisms in polyethylene pipes and the primary cause of their long-term failure, the slow crack growth (SCG) phenomenon. This failure mode in high-density polyethylene (HDPE) pipelines can potentially lead to brittle-like fractures without any localized yielding or plastic deformation. Leveraging the ISO 9080:2012 standard for estimating pipe service life under SCG, we employ a reliability-based assessment incorporating Monte-Carlo simulations to explore the impact of geometric burial design and soil characteristics on pipeline integrity.Variables including pipe diameter, depth of cover, bedding angle, trench width, soil type, and compaction level were analyzed to determine their impact on the progression toward medium and high SCG-mediated probability of failure levels. In addition to a thorough examination of these variables, a machine-learning analysis was employed as a supplementary tool to contrast and quantify their significance in influencing the time of entry into medium and high probability of failure states. This layered approach, combining detailed variable analysis with machine learning insights, underscores the complex interplay of design and environmental factors in mitigating SCG probability of failure.Our research underscores that bedding angles under 90° substantially increase the likelihood of SCG-induced failures in HDPE pipelines.  Although additional factors studied also affect failure probabilities, their impact is less critical in comparison to that of the bedding angle. By optimizing these lesser factors, minimal failure probabilities can be sustained across the standard 50-year service life of HDPE pipelines, thus bolstering their durability and reliability across diverse operating environments.

Keywords: HDPE pipelines, Slow crack growth (SCG), Pipeline reliability, Buried Pipeline Design, Soil characteristics, Monte Carlo simulation