1. A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring. S. Bloemheuvel, J. Hoogen and M. Atzmueller. Applied Network Science 2021. paper
2. On an Application of Graph Neural Networks in Population-Based SHM. G. Tsialiamanis, C. Mylonas, E. Chatzi, D.J. Wagg, N. Dervilis& K. Worden. Data Science in Engineering 2021. paper
3. Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces. G. Tsialiamanis, C. Mylonas, E. Chatzi, N. Dervilis, D.J. Wagg, K. Worden. Mechanical Systems and Signal Processing 2021. paper
4. Novelty detection of cable-stayed bridges based on cable force correlation exploration using spatiotemporal graph convolutional networks. S. Li, J. Niu, Z. Li. Structural Health Monitoring 2021. paper
5. Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network. H. Son, V. Pham, Y. Jang, and S. Kim. Sensors 2021. paper
6. Restoration of missing structural health monitoring data using spatiotemporal graph attention networks. J. Niu, S. Li, Z. Li. Structural Health Monitoring 2022. paper

1. Mist-edge-fog-cloud computing system for geometric and thermal error prediction and compensation of worm gear machine tools based on ONT-GCN spatial-temporal model. H. Gui, J. Liu, C. Ma, M. Li, S. Wang. Mechanical Systems and Signal Processing 2023. paper
2. EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks. L. Zhou, S. Chen, Y, Ni, A. Choy. Smart Materials and Structures 2021. paper
3. Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation. L. Zhou, S. Chen, Y. Ni1,2 and L. Jiang. Smart Materials and Structures 2022. paper
4. Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation. J. Narwariya, P. Malhotra, V. TV, L. Vig, G. Shroff. AAAI workshop DLGMA'20. paper
5. GCG: Graph Convolutional network and gated recurrent unit method for high-speed train axle temperature forecasting. J. Man, H. Dong, X. Yang, Z. Meng, L. Jia, Y. Qin, G. Xin. Mechanical Systems and Signal Processing 2022. paper
6. Semi-supervised graph convolutional network to predict position and speed-dependent tool tip dynamics with limited labeled data. C Qiu a, K. Li, B. Li, X. Mao, S. He, C. Hao a,d, Ling Yin. Mechanical Systems and Signal Processing 2022. paper

1. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. D. Zhang, E. Stewart, M.Entezami, C. Roberts D. Yu. Measurement 2020. paper
2. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study. T. Li, Z. Zhou, S. Li, C. Sun, R. Yan, X. Chen. Mechanical Systems and Signal Processing 2022. paper
3. A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines. B. Zhao, X. Zhang, Q. Wu, Z. Yang, Z. Zhan. Mechanical Systems and Signal Processing 2023. paper

1. Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. H. Gao, M. Zahr, J. Wang. Computer Methods in Applied Mechanics and Engineering 2022. paper

1. Learning to simulate and design for structural engineering. K. Chang, C. Cheng. ICML2020. paper
2. Inferring CAD Modeling Sequences Using Zone Graphs. X. Xu, W. Peng, C. Cheng, K. Willis, D. Ritchie. CVPR2021. paper

To be continued