Identifying Accelerated Ageing Pathways for Cross-Linked Polyethylene Pipes Through Machine Learning

Cross-linked polyethylene (PEX) pipes are increasingly being used in domestic and industrial settings to transport water, gas and sewage. It is important to understand changes to the polymer and additive compounds with in-service use. We used infrared (IR) microscopy, combining the chemical specificity of IR spectroscopy with the spatial resolution of light microscopy, to track variations in the degree of crystallinity, additive concentrations as well as various chemical species across the wall thickness of PEX pipes. We have shown that principal component analysis of IR absorbance peaks can be used to classify different pipe formulations [1]. We used this methodology to characterize changes to pipes subjected to accelerated aging protocols designed to exaggerate conditions experienced by pipes during in-service use. This allowed us to identify and track IR peaks that are most relevant to pipe degradation. We used these results, together with decision tree and random forest classification algorithms, to identify different modes of pipe degradation and to better understand ageing effects on the long-term stability of PEX pipes.

[1] M. Hiles et al., J. Polym. Sci. Pol. Phys. 57, 1255–1262 (2019).