This study develops an embedded impact damage identification method for composite structures using laser-induced graphene (LIG) combined with electrical impedance tomography (EIT) and deep learning. An LIG sensing area was directly fabricated on a polyimide-based flexible printed circuit (FPC) via a CO2 laser and embedded within the interlayer region of glass fiber reinforced polymer composites. This LIG-FPC sensor serves as a highly sensitive sensing layer for capturing localized conductivity change induced by impact damage. After impact, the change in the pathways of the conductive network results in measurable change in the boundary voltages. The boundary voltage change was acquired by an EIT system and processed by a modified residual network (ResNet18) to reconstruct internal conductivity change distribution. A simulation-based dataset mapping boundary voltage changes to conductivity change distributions was used to train the network. Experimental results demonstrate that the proposed method can precisely localize damage. This approach effectively overcomes limitations associated with traditional sensor-based methods, providing a robust, fast, and non-destructive solution for in-situ structural health monitoring (SHM) of composite structures.
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.…
TL;DR: A framework for CVSHM, which allows for robust detection, localization, and assessment of the damage even for highly contaminated displacement data, is proposed and tested using realistic synthetic videos representing vibrating truss structure.
Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a poss…
Visual spectrum cameras have become increasingly popular for non-contact full-field structural dynamics measurements, enabling displacement and deformation identification through techniques such as Digital Image Correlation. However, obtaining strain information from kinematic me…
TL;DR: A framework that combines unsupervised learning and domain adaptation to enhance model transferability under limited data, reducing dependence on labeled datasets while preserving sensitivity to structural and operational changes is proposed.
Abstract: The scarcity of long-term vibration data real-world structures remains a significant barrier to the application of machine learning in structural health monitoring (SHM). Available datasets are often short, unlabeled, and affected by operational and environmental variab…
The structural integrity of railway rails is essential for the safety and efficiency of modern transportation networks, where early detection of damage is crucial to preventing catastrophic failures and service disruptions. Guided wave-based structural health monitoring (SHM) off…
Load identification is a crucial topic in structural health monitoring (SHM). Existing approaches involve a trade-off between the amount of data required and the fidelity of available parametric physical models. Purely data-driven methods require extensive labeled training data f…