Defects Identification and Crack Depth Determination in Porous Media on the Brick Masonry Example Using Ultrasonic Methods: Numerical Analysis and Machine Learning

Automation of the structural health monitoring process involves the use of successful methods for detecting defects and determining their critical characteristics. An efficient means of crack detection in composite materials is the ultrasonic method, but its application to determine critical crack parameters, such as depth in construction practice, is difficult or leads to large errors. This article focuses on machine learning methods usage to detect cracks in composite materials like brickwork. Ceramic bricks with various mechanical properties and with pre-grown cracks from 2 to 60 mm are considered. To understand the processes occurring during the ultrasonic pulse transmission, modeling was performed in the ANSYS environment. The brick is considered a porous medium weakened by a crack. Numerical modeling allows for the identification of the main features of the signal response and the determination of the amplitude-time range for different porosity and crack depth values. Using machine learning methods made it possible to solve two related problems. The first, binary classification, i.e., the presence or absence of a crack, is solved with 100% accuracy. The second is determining the crack depth. A neural network was built using an ensemble of decision trees. The accuracy of crack depth prediction is R2 = 0.983, and the error in predicted values is within 8%.