Abstract
Structural health monitoring (SHM) using IoT sensor devices plays a crucial role in the preservation of civil structures. SHM aims at performing an accurate damage diagnosis of a structure, that consists of identifying, localizing, and quantify the condition of any significant damage, to keep track of the relevant structural integrity. Deep learning (DL) architectures have been progressively introduced to enhance vibration-based SHM analyses: supervised DL approaches are integrated into SHM systems because they can provide very detailed information about the nature of damage compared to unsupervised DL approaches. The main drawback of supervised approach is the need for human intervention to appropriately label data describing the nature of damage, considering that in the SHM context, providing labeled data requires advanced expertise and a lot of time. To overcome this limitation, a key solution is a digital twin relying on physics-based numerical models to reproduce the structural response in terms of the vibration recordings provided by the sensor devices during a specific events to be monitored. This work presents a comprehensive methodology to carry out the damage localization task by exploiting a convolutional neural network (CNN) and parametric model order reduction (MOR) techniques to reduce the computational burden associated with the construction of the dataset on which the CNN is trained. Experimental results related to a pilot application involving a sample structure, show the potential of the proposed solution and the reusability of the trained system in presence of different loading scenarios.
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- 1.
Deck, Dynamic Displacement Sensor. Move Srl, Italy. https://www.movesolutions.it/deck/.
References
Aparicio, J., Jiménez, A., Ureña, J., Alvarez, F.J.: Realistic modeling of underwater ambient noise and its influence on spread-spectrum signals. In: OCEANS 2015-Genova, pp. 1–6. IEEE (2015)
Aydemir, H., Zengin, U., Durak, U.: The digital twin paradigm for aircraft review and outlook. In: AIAA Scitech 2020 Forum, p. 0553 (2020)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2) (2012)
Bisong, E.: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Springer, Cham (2019). https://doi.org/10.1007/978-1-4842-4470-8
Cimino., M., Galatolo., F., Parola., M., Perilli., N., Squeglia., N.: Deep learning of structural changes in historical buildings: the case study of the Pisa tower. In: Proceedings of the 14th International Joint Conference on Computational Intelligence, INSTICC, pp. 396–403. SciTePress (2022)
Galatolo, F.A., Cimino, M.G.C.A., Vaglini, G.: Using Stigmergy to incorporate the time into artificial neural networks. In: Groza, A., Prasath, R. (eds.) MIKE 2018. LNCS (LNAI), vol. 11308, pp. 248–258. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05918-7_22
Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B.: Characterising the digital twin: a systematic literature review. CIRP J. Manuf. Sci. Technol. 29, 36–52 (2020)
Li, D., Zhang, J., Zhang, Q., Wei, X.: Classification of ECG signals based on 1D convolution neural network. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6. IEEE (2017)
Paolucci, R., Gatti, F., Infantino, M., Smerzini, C., Özcebe, A.G., Stupazzini, M.: Broadband ground motions from 3D physics-based numerical simulations using artificial neural networksbroadband ground motions from 3D PBSS using ANNs. Bull. Seismol. Soc. Am. 108(3A), 1272–1286 (2018)
Parola, M.: Damage localization task source code and data. https://github.com/topics/structural-health-monitoring
Parola., M., Galatolo., F., Torzoni., M., Cimino., M., Vaglini., G.: Structural damage localization via deep learning and IoT enabled digital twin. In: Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA, INSTICC, pp. 199–206. SciTePress (2022). https://doi.org/10.5220/0011320600003277
Quarteroni, A., Manzoni, A., Negri, F.: Reduced Basis Methods for Partial Differential Equations: An Introduction, vol. 92. Springer, Cham (2015)
Rosafalco, L., Torzoni, M., Manzoni, A., Mariani, S., Corigliano, A.: Online structural health monitoring by model order reduction and deep learning algorithms. Comput. Struct. 255, 106604 (2021)
Sabetta, F., Pugliese, A.: Estimation of response spectra and simulation of nonstationary earthquake ground motions. Bull. Seismol. Soc. Am. 86(2), 337–352 (1996)
Toh, G., Park, J.: Review of vibration-based structural health monitoring using deep learning. Appl. Sci. 10(5), 1680 (2020)
Torzoni, M., Manzoni, A., Mariani, S.: Structural health monitoring of civil structures: a diagnostic framework powered by deep metric learning. Comput. Struct. 271, 106858 (2022). https://doi.org/10.1016/j.compstruc.2022.106858
Torzoni, M., Manzoni, A., Mariani, S.: A deep neural network, multi-fidelity surrogate model approach for Bayesian model updating in SHM. In: Rizzo, P., Milazzo, A. (eds.) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol. 254, pp. 1076–1086. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-07258-1_108
Torzoni, M., Rosafalco, L., Manzoni, A.: A combined model-order reduction and deep learning approach for structural health monitoring under varying operational and environmental conditions. Eng. Proc. 2(1), 94 (2020)
Torzoni, M., Rosafalco, L., Manzoni, A., Mariani, S., Corigliano, A.: SHM under varying environmental conditions: an approach based on model order reduction and deep learning. Comput. Struct. 266, 106790 (2022). https://doi.org/10.1016/j.compstruc.2022.106790
Wang, X., et al.: Probabilistic machine learning and Bayesian inference for vibration-based structural damage identification (2022)
Ye, X., Jin, T., Yun, C.: A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct. Syst. 24(5), 567–585 (2019)
Yiğitler, H., Badihi, B., Jäntti, R.: Overview of time synchronization for IoT deployments: clock discipline algorithms and protocols. Sensors 20(20), 5928 (2020)
Yu, T., Zhu, H.: Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint: arXiv:2003.05689 (2020)
Acknowledgements
This work has been partially supported by: (i) the University of Pisa, in the framework of the PRA_2022_101 project “Decision Support Systems for territorial networks for managing ecosystem services”; (ii) the Tuscany Region, in the framework of the“SecureB2C” project, POR FESR 2014–2020, Law Decree 7429 31.05.2017; (iii) the Italian Ministry of Education and Research (MIUR), in the framework of the FoReLab project (Departments of Excellence). The authors are grateful to the research team at Politecnico di Milano composed of Alberto Corigliano, Andrea Manzoni, Luca Rosafalco and Stefano Mariani, for several insightful discussions about this research.
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Parola, M., Galatolo, F.A., Torzoni, M., Cimino, M.G.C.A. (2023). Convolutional Neural Networks for Structural Damage Localization on Digital Twins. In: Fred, A., Sansone, C., Gusikhin, O., Madani, K. (eds) Deep Learning Theory and Applications. DeLTA 2022. Communications in Computer and Information Science, vol 1858. Springer, Cham. https://doi.org/10.1007/978-3-031-37317-6_5
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