Predicting Service Life of Polyethylene Pipes under Crack Expansion using Random Forest Method

The study of factors influencing the performance of PE pipe against rapid crack expansion is of great significance for the safe use of PE pipe. This paper analyzes the role of each step in the algorithm based on the theoretical basis of random forest, and proposes an improved random forest method based on recursive feature elimination by changing the node splitting rules to address the shortcomings of the random forest classification accuracy. The method is used to analyze the effect of rapid crack expansion of PE pipe in terms of pipe size and wall thickness, impact knife speed, and notched impact strength of simply supported beams. Under the same conditions, the extended crack lengths of DN260, DN150 and DN65 pipes are 197, 164 and 128 mm, respectively, while the crack lengths of PE80 pipes are 24, 210 and 239 mm at impact knife speeds of 10, 15 and 20 m/s, respectively. The higher the notched impact strength of the simple beam, the higher the critical pressure value and the better the RCP resistance. The study of rapid crack expansion of PE pipe based on deep learning algorithm can identify the main internal and external factors affecting the RCP resistance of PE pipe and provide a solid basis for PE pipe life prediction.