Ultrasonic Guided Waves (UGWs) are among the most effective tools for damage diagnosis and Structural Health Monitoring (SHM) of thin-walled structures. However, traditional SHM methods based on UGWs typically require baseline measurements and signal post-processing to extract damage indices, which can lead to the loss of important information contained in the raw data. To face these challenges, recent studies have investigated machine learning approaches. However, most of these methods depend on large labeled datasets and use black-box models with limited interpretability. In this work, a Physics-Informed Neural Network (PINN) framework is proposed to overcome these limitations. The PINN solves an inverse problem by simultaneously reconstructing the full ultrasonic wavefield and the spatial distribution of the wave velocity, expressed in terms of Young’s modulus, recognizing potential discontinuities caused by damage. This is achieved by minimizing a combined loss function that enforces both the agreement with sparse measurement data and the physical laws governing the propagation of UGWs. The proposed method is numerically validated on a case study of an isotropic structure affected by damage. The results demonstrate that the approach can accurately localize damage without the need for data post-processing. Moreover, the method is fully unsupervised and baseline-free, relying solely on sparse measurements and the underlying physical laws of UGW propagation.
Recent advances in Physics-Informed Neural Networks (PINNs) have opened new possibilities for integrating structural dynamics and data-driven learning in Structural Health Monitoring (SHM). This work presents a physics-informed framework for input load estimation and virtual sens…
TL;DR: The results demonstrate that PINN achieves more accurate and stable full-field vibration reconstructions than conventional PINNs, particularly under conditions involving high-frequency modes, and highlights the potential of hybrid data-physics neural frameworks as an efficient and reliable approach for solving complex PDE-governed dynamical systems.
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm in scientific machine learning by embedding governing physical laws into neural network training through loss functions. They have demonstrated remarkable success in solving various forward and inverse p…
Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides detailed information about their structural integrity and overall condition. Due to their intricated characteristics, UGWs are…
TL;DR: An input-robust hybrid physics informed neural network (rHPINN) framework is proposed that integrates physics-based system dynamics with the temporal learning capability of HPINN, allowing accurate estimation of system states and spatial health parameters without input force measurements.
System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-dri…
A physics-informed neural network (PINN) is developed for modeling time-harmonic Lamb-wave excitation in a two-dimensional elastic waveguide under surface loading. The displacement and stress fields are represented by the network, and its trainable weights are determined by enfor…
Ultrasonic guided waves are widely used for monitoring composite panels due to their high sensitivity to structural changes. While numerous damage indices (DIs) have been proposed to detect and localize damage, they are typically applied either independently, ignoring the potenti…