The Potential for Material Property Characterization Using Physics-Informed Neural Networks and Ultrasonic Wave Data

• Published in: Research in nondestructive evaluation Vol. 35; no. 4; pp. 211 - 231
• Main Authors: Lee, Sangmin, Popovics, John S.
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03-07-2024; Taylor & Francis Ltd
• Subjects: Artificial neural networks; Concrete; Data analysis; Data collection; Defects; Energy dissipation; Heterogeneity; Machine learning; material characterization; Material properties; Neural networks; Nondestructive testing; PINN; Structural analysis; Structural members; Ultrasonic testing; ultrasound; wave propagation; Wave velocity
• ISSN: 0934-9847; 1432-2110
• DOI: 10.1080/09349847.2024.2350398

There is a need for reliable nondestructive test methods that can collect data from structural members and analyze the results in a rapid and efficient manner. Large amounts of test data are needed to achieve such characterization, which provides additional challenges because of their heterogeneity and complexity. Advances in machine learning, in particular physics-informed neural networks (PINN), offer potential to address these problems. PINN is a particular form of artificial neural networks (ANN) and portends notable advantages over traditional measurand analysis or purely data-driven approaches. Here, we explore the potential of heterogeneous material property characterization using PINN and ultrasonic wave data. First, several types of 1-D ultrasonic wave data are numerically simulated for a spatially heterogeneous material, and then PINN is applied to predict wave velocity, defect location, and energy dissipation. Then, three different types of defects are simulated and all defects are detected using the corresponding 2-D ultrasonic wave data and PINN. The presented results demonstrate the promise of PINN to assist with heterogeneous material characterization methods.