An Uncertainty-aware Intelligent Digital Twin Framework for Robust Structural Health Monitoring
Keywords:
Bayesian uncertainty, Digital twin, Neural networks, Physics-informed learning, Risk assessment, Spatio-temporal graph, Structural health monitoringAbstract
Civil infrastructure is continuously exposed to operational loads, environmental effects, and material degradation, making accurate Structural Health Monitoring (SHM) essential for ensuring safety and reliability. Conventional SHM methods rely on periodic manual inspections or purely data-driven models, which often lack robustness and fail to generalize under changing operating conditions. The objective of this study is to develop an intelligent digital twin framework that improves damage prediction accuracy while providing reliable uncertainty estimates for informed maintenance decision-making. To achieve this, an uncertainty-aware physics-guided digital twin framework, named UPG-DT, is proposed. The framework integrates physics-guided representation learning, spatio-temporal graph neural networks, and Bayesian uncertainty estimation to capture structural behavior, model spatial correlations among multi-sensor data, and quantify prediction uncertainty. Experimental evaluation on real-world benchmark datasets demonstrates that UPG-DT outperforms existing approaches in predictive accuracy, reduces false-positive detections, and provides better uncertainty calibration. In addition, the proposed framework combines damage predictions and uncertainty estimates into a hybrid risk score that supports maintenance prioritization. These results indicate that UPG-DT offers a reliable and practical solution for intelligent SHM and digital twin-based infrastructure management.
References
C. R. Farrar and K. Worden, “An introduction to structural health monitoring,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, no. 1851, pp. 303–315, 2007.
K. Worden C. R. Farrar, G. Manson, and G. Park, “The fundamental axioms of structural health monitoring, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 463, no. 2082, pp. 1639–1664, 2007.
Y-J Cha, W. Choi, O. Büyüköztürk, “Deep learning‐based crack damage detection using convolutional neural networks,” Computer‐Aided Civil and Infrastructure Engineering, vol. 32, no. 5, pp. 361–378, Mar. 2017.
J. Jia and Y. Li, “Deep learning for structural health monitoring: Data, algorithms, applications, challenges, and trends,” Sensors, vol. 23, no. 21, Oct. 2023.
M. Grieves, “Digital twin: manufacturing excellence through virtual factory replication,” Whitepaper, 2014.
X. Liu, “A systematic review of digital twin about physical entities, virtual models, twin data, and applications,” Advanced Engineering Informatics, vol. 55, Jan. 2023.
Z. Sun, S. Jayasinghe, A. Sidiq, F. Shahrivar, M. Mahmoodian, and S. Setunge, “Approach towards the development of digital twin for structural health monitoring of civil infrastructure: A comprehensive review,” Sensors, vol. 25, no. 1, Dec. 2024.
Z. Lei, Y. Liu, K. Ding, H. Chen, and D. Li, “Digital twin-driven structural thermal deformation compensation system for radio telescopes,” Journal of System Simulation, vol. 36, no. 2024.
M. Raissi, P. Perdikaris, G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686–707, Feb. 2019.
M. Zhang, T. Guo, G. Zhang, Z. Liu, W. Xu, “Physics-informed deep learning for structural vibration identification and its application on a benchmark structure,” Philosophical Transactions of the Royal Society A, vol. 382, no. 2264, 2024.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv, 2017.
X. Ma et al., “A comprehensive survey on graph anomaly detection with deep learning,” in IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12012–12038, 1 Dec. 2023.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” arXiv, 2018.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” International conference on Machine Learning, 2016, pp. 1050–1059.
A. Kendall and Y. Gal, “What uncertainties do we need in Bayesian deep learning for computer vision?,” Advances in Neural Information Processing Systems, 2017.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” Proceedings of the 34th International Conference on Machine Learning, Aug. 2017, pp. 1321–1330.
G. W. Glenn, “Verification of forecasts expressed in terms of probability,” Monthly Weather Review, vol. 78, no. 1, Jan. 1950.
J. Xu, H. Wu, J. Wang, and M. Long, “Anomaly transformer: Time series anomaly detection with association discrepancy,” arXiv, 2022.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization, arXiv, 2014.
A. Paszke, “Pytorch: An imperative style, high-performance deep learning library,” arXiv, 2019.
S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, vol. 4, no. 1, pp. 1–58, 1992.
L. Das, B. Gjorgiev, G. Sansavini, “Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines,” arXiv, 2023.
