Physics-Informed Machine Learning for Damage Localization and Progression in Composite Structures
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https://orcid.org/0000-0002-9546-1488
Abstract
Composite materials are widely used in lightweight structures, but their heterogeneous architecture and interacting damage mechanisms make damage progression difficult to predict. As a result, composite components are often designed with conservative safety margins, increasing weight, cost, and inspection effort. Improved methods for structural health monitoring and damage-progression analysis are therefore needed to support earlier damage detection, residual-life assessment, and more efficient structural design.
This research develops physics-informed machine-learning methods for monitoring and modeling damage evolution in composites. We propose a framework that addresses three complementary challenges: (1) damage detection and localization from sparse strain measurements using graph neural networks that generalize to unseen damage locations and sensor layouts, (2) microscale stress prediction in damaged composite microstructures using a deep-learning surrogate that can be integrated into micro-macro simulation workflows, and (3) macroscale damage modeling using a topologically informed neural network. Together, these contributions form a multiscale learning framework linking sparse experimental observations, microscale material response, and macroscale laminate damage evolution.
How to Cite
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Physics-Informed, GNN, Composites, Damage, Localization, Simulation, FEM, Monitoring
Del Priore, E., Sbarra, R. G., Coppotelli, G., Lampani, L., & Pasquali, M. (2025). Damage Identification in Aeronautical Structures Using Strain Mode Shapes and Automatic Operational Modal Analysis. Journal of Vibration Engineering & Technologies, 13(8), 567.
arban, M. R. G., Chemisky, Y., Pruliere, E., & Clement, M. (2025). Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity. arXiv.
Ghosh, G., Biswas, D., & Bhattacharyya, R. (2025). Advancements in multiscale modeling of damage in composite materials: A comprehensive review. Composites Part B: Engineering, 307, 112819.
Hinton, M., & Kaddour, A. (2012). The background to the SecondWorld-Wide Failure Exercise. Journal of Composite Materials, 46(19-20), 2283–2294.
Iparraguirre, M. M., Alfaro, I., Gonzalez, D., & Cueto, E. (2026). MeshGraphNet-Transformer: Scalable Meshbased Learned Simulation for Solid Mechanics. arXiv.
Mylonas, C., Tsialiamanis, G., Worden, K., & Chatzi, E. N. (2023). Bayesian graph neural networks for strainbased crack localization. arXiv.
Ogunleye, R. O., Rusn´akov´a, S., Javorık, J., Zaludek, M., & Kotlanova, B. (2024). Advanced Sensors and Sensing Systems for Structural Health Monitoring in Aerospace Composites. Advanced Engineering Materials, 26(22), 2401745.
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., & Battaglia, P. W. (2021). LEARNING MESH-BASED SIMULATION WITH GRAPH NETWORKS.
Saxena, A., Roychoudhury, I., Celaya, J., Saha, S., Saha, B., & Goebel, K. (2010). Requirements Specification for Prognostics Performance - An Overview. In AIAA Infotech@ Aerospace 2010.
Singh, T., & Sehgal, S. (2022). Structural Health Monitoring of Composite Materials. Archives of Computational Methods in Engineering, 29(4), 1997–2017.
Wei, A., & Fink, O. (2025). Integrating physics and topology in neural networks for learning rigid body dynamics. Nature Communications, 16(1), 6867.
Yacouti, M., & Shakiba, M. (2025). Integrated convolutional and graph neural networks for predicting mechanical fields in composite microstructures. Composites Part A: Applied Science and Manufacturing, 190, 108618.
Yang, Z., Pei, C., Yan, H., & Long, L. (2020). Fatigue damage modeling of ceramic-matrix composites: A short review. Material Design & Processing Communications, 2(2), e129.
Zhou, Y., & Semnani, S. J. (2025). A machine learning based multi-scale finite element framework for nonlinear composite materials. Engineering with Computers, 41(5), 2795–2831.
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