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 enforcing the first-order elastodynamic system, the traction boundary conditions, and the Lamb-mode-based Dirichlet-to-Neumann (DtN) conditions in the loss function. This formulation avoids derivative boundary constraints and enables direct extraction of modal amplitudes and far-field responses. The method is validated against an analytical multimodal solution for uniform shear-stress excitation. For representative single-frequency cases, the PINN reproduces the near-field displacement patterns and projected modal coefficients with good accuracy. A frequency-parameterized model is further trained to predict broadband responses and captures the main trends of the propagating modes with moderate errors over the considered band. The proposed model provides an extensible framework for broadband guided-wave forward modeling, with potential applications in transducer design optimization.
TL;DR: This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.
While neural networks represent a promising approach for evaluating sensor data to assess damage presence, location and severity, large amounts of data are required for training. However, the generation of experimental data is both labor-intensive and costly. Transfer learning is…
TL;DR: This work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures.
This paper presents an integrated Structural Health Monitoring (SHM) strategy for flax fiber reinforced thermoplastic composites, combining Nonlinear Resonance Acoustic Spectroscopy (NLRAS), Acoustic Emission (AE), and data-driven damage identification based on machine learning.…
Accurate prediction of the structural temperature field is crucial for the static and dynamic monitoring of engineering structures, with particular significance for heritage buildings where material preservation is paramount. The complex, time-lagged, and non-linear relationship…
Online Health Management (HM) plays a pivotal role in optimizing the lifecycle of aircraft while ensuring safety, reliability, and structural integrity. During service, aircraft structures experience complex cyclic loading, making accurate load analysis essential for effective li…
Fatigue in metallic materials leads to progressive degradation driven by a sequence of microstructural mechanisms occurring over the life cycle. While fracture is typically the most obvious and critical damage state, it only appears at the end of life. However, when no fracture i…
Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point,…