Machine learning supported ultrasonic testing for characterization of cracks in polyethylene pipes

This study aims to develop a machine learning supported ultrasonic guided wave testing (UGWT) for detection and characterization of cracks in polyethylene (PE) pipes used for natural gas distribution. Ultrasonic testing is conducted to investigate the wave-crack interaction and determine damping properties of PE pipes. Finite element models are further built with material properties fine-tuned by wave attenuation experiments combined with cross-correlation analysis between the simulated and experimental signals. A synthetic database is populated using sensed signals over a wide range of crack geometries from numerical simulations. Finally, support vector machine (SVM) models are developed with different feature selections for classification of crack geometry and the accuracy is evaluated using both simulated and experimental cases. The findings demonstrate the potential of locating the circumferential position of crack with the ring-focusing method and classifying crack geometry in PE pipes using SVM model with central frequency features.