Real-Time Multi-Modal SHM Framework For Complex Infrastructure
DOI:
https://doi.org/10.29284/d2tvab57Keywords:
Structural Health Monitoring, Model Order Reduction, Artificial Neural Networks, Deep Learning, Digital Twin, Multi-Fidelity Modeling, Infrastructure Resilience.Abstract
Advanced real-time structural health monitoring (SHM) of complex infrastructures like bridges, tunnels, and high-rise buildings, has been on the rise due to the age of assets, the growing strain on the environment, and the rapid urbanization process. Traditional SHM methodologies rely on expensive sensor networks and high-fidelity finite-element (FE) simulations, which are computationally expensive and hence will do not support real-time usage. Through the combination of reduced-order modeling with artificial intelligence (AI), it is possible to attain a disruptive solution, with a compromise of accuracy and computation cost. The current research problem is related to the creation and validation of a multi-model SHM framework, which integrates model-order reduction (MOR) with artificial neural networks (ANNs) and deep-learning-driven digital twins in a synergistic manner. The whole scenario is to enable ongoing, predictive monitoring and maintenance of critical infrastructures as well as improving computation efficiency and making well-informed decisions.
References
[1] 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, Feb. 2007, doi: 10.1098/rsta.2006.1928.
[2] C. R. Farrar and K. Worden, Structural Health Monitoring. Wiley, 2012. doi: 10.1002/9781118443118.
[3] J. M. W. Brownjohn, “Structural health monitoring of civil infrastructure,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, no. 1851, pp. 589–622, Feb. 2007, doi: 10.1098/rsta.2006.1925.
[4] S. C. Jayasinghe et al., “A review on the applications of artificial neural network techniques for accelerating finite element analysis in the civil engineering domain,” Comput Struct, vol. 310, p. 107698, Apr. 2025, doi: 10.1016/j.compstruc.2025.107698.
[5] M. Torzoni, A. Manzoni, and S. Mariani, “A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks,” Mech Syst Signal Process, vol. 197, p. 110376, Aug. 2023, doi: 10.1016/j.ymssp.2023.110376.
[6] F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, “Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison,” Engineering, vol. 5, no. 4, pp. 653–661, Aug. 2019, doi: 10.1016/j.eng.2019.01.014.
[7] H. Sohn, C. R. Farrar, F. Hemez, and J. Czarnecki, “A Review of Structural Health,” 1996. Accessed: Dec. 27, 2025. [Online]. Available: https://api.semanticscholar.org/CorpusID:65173467
[8] T. Rubert et al., “Wind turbine lifetime extension decision-making based on structural health monitoring,” Renew Energy, vol. 143, pp. 611–621, Dec. 2019, doi: 10.1016/j.renene.2019.05.034.
[9] B. F. Spencer, M. E. Ruiz-Sandoval, and N. Kurata, “Smart sensing technology: opportunities and challenges,” Struct Control Health Monit, vol. 11, no. 4, pp. 349–368, Oct. 2004, doi: 10.1002/stc.48.
[10] D. Fusco, C. Rinaldi, D. Addessi, and V. Gattulli, “Advanced Fiber Beam Finite Element Model for Neural Network Training in Vibration-Based Bridge Monitoring,” Procedia Structural Integrity, vol. 62, pp. 895–902, 2024, doi: 10.1016/j.prostr.2024.09.120.
[11] P. Benner, S. Gugercin, and K. Willcox, “A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems,” SIAM Review, vol. 57, no. 4, pp. 483–531, Jan. 2015, doi: 10.1137/130932715.
[12] A. Quarteroni, A. Manzoni, and F. Negri, Reduced Basis Methods for Partial Differential Equations, vol. 92. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-15431-2.
[13] B. Peherstorfer, K. Willcox, and M. Gunzburger, “Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,” SIAM Review, vol. 60, no. 3, pp. 550–591, Jan. 2018, doi: 10.1137/16M1082469.
[14] B. Khan, S. M. Ali, and Z. Ullah, “Deep learning based digital twins augmented reality: Model predictive control for battery and storage optimization in renewable energy prosumers districts,” J Energy Storage, vol. 131, p. 117565, Sep. 2025, doi: 10.1016/j.est.2025.117565.
[15] J. F. S. Neto et al., “Supervised and unsupervised deep learning applied to the majority vote model,” Physica A: Statistical Mechanics and its Applications, vol. 683, p. 131208, Feb. 2026, doi: 10.1016/j.physa.2025.131208.
[16] S. Boschert and R. Rosen, “Digital Twin—The Simulation Aspect,” in Mechatronic Futures, Cham: Springer International Publishing, 2016, pp. 59–74. doi: 10.1007/978-3-319-32156-1_5.
[17] Hemant Kumar Thakur and Rahul Kumar, “Structural Health Monitoring in India: Progress and Prospects (2000-2025),” International Journal of Interdisciplinary Innovative Research & Development, doi: 10.5281/zenodo.16279811.
[18] M. Abbasi, A. Consilvio, I. A. Hernández-González, P. Lopez-Arevalo, and H. Alavi, “On supporting traffic management decisions according to bridges structural health monitoring within a digital twin-oriented platform,” Transportation Research Procedia, vol. 90, pp. 384–391, 2025, doi: 10.1016/j.trpro.2025.06.129.
[19] E. Glaessgen and D. Stargel, “The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles,” in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR>20th AIAA/ASME/AHS Adaptive Structures Conference<BR>14th AIAA, Reston, Virigina: American Institute of Aeronautics and Astronautics, Apr. 2012. doi: 10.2514/6.2012-1818.
[20] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
[21] G.-Q. Zhang, B. Wang, J. Li, and Y.-L. Xu, “The application of deep learning in bridge health monitoring: a literature review,” Advances in Bridge Engineering, vol. 3, no. 1, p. 22, Dec. 2022, doi: 10.1186/s43251-022-00078-7.
[22] C. R. Farrar, S. W. Doebling, and D. A. Nix, “Vibration-Based Structural Damage Identification,” Philosophical Transactions: Mathematical, Physical and Engineering Sciences, vol. 359, no. 1778, pp. 131–149, 2001, Accessed: Dec. 27, 2025. [Online]. Available: https://www.jstor.org/stable/3066397
[23] P. Cawley and R. D. Adams, “The location of defects in structures from measurements of natural frequencies,” J Strain Anal Eng Des, vol. 14, no. 2, pp. 49–57, Apr. 1979, doi: 10.1243/03093247V142049.
[24] M. I. Friswell and J. E. Mottershead, Finite Element Model Updating in Structural Dynamics, vol. 38. Dordrecht: Springer Netherlands, 1995. doi: 10.1007/978-94-015-8508-8.
[25] A. C. Antoulas, Approximation of Large-Scale Dynamical Systems. Society for Industrial and Applied Mathematics, 2005. doi: 10.1137/1.9780898718713.
[26] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, Cham: Springer International Publishing, 2017, pp. 85–113. doi: 10.1007/978-3-319-38756-7_4.
[27] S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” Mar. 2015.
[28] Hemant Kumar Thakur and Dr. Rahul Kumar, “Deep vision techniques for structural health monitoring Uncertainty in condition,” African Journal of Biomedical Research, vol. 27, no. 6S, pp. 1763–1778, Dec. 2024, doi: 10.53555/AJBR.v27i6S.8803.
[29] M. C. Kennedy and A. O’Hagan, “Bayesian Calibration of Computer Models,” J R Stat Soc Series B Stat Methodol, vol. 63, no. 3, pp. 425–464, Sep. 2001, doi: 10.1111/1467-9868.00294.
[30] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J, vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198.
[31] R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” IEEE Trans Neural Netw, vol. 9, no. 5, pp. 1054–1054, Sep. 1998, doi: 10.1109/TNN.1998.712192.
[32] R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Mach Learn, vol. 8, no. 3–4, pp. 229–256, May 1992, doi: 10.1007/BF00992696.
[33] A. Jierula, S. Wang, T.-M. OH, and P. Wang, “Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data,” Applied Sciences, vol. 11, no. 5, p. 2314, Mar. 2021, doi: 10.3390/app11052314.
[34] M. Torzoni, M. Tezzele, S. Mariani, A. Manzoni, and K. E. Willcox, “A digital twin framework for civil engineering structures,” Oct. 2023, doi: 10.1016/j.cma.2023.116584.
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