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Open AccessProceedings

A Hybrid Structural Health Monitoring Approach Based on Reduced-Order Modelling and Deep Learning

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Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
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MOX, Dipartimento di Matematica, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
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Author to whom correspondence should be addressed.
Presented at the 6th International Electronic Conference on Sensors and Applications, 15–30 November 2019; Available online: https://ecsa-6.sciforum.net/.
Proceedings 2020, 42(1), 67; https://doi.org/10.3390/ecsa-6-06585
Published: 21 April 2020
Recent advances in sensor technologies coupled with the development of machine/deep learning strategies are opening new frontiers in Structural Health Monitoring (SHM). Dealing with structural vibrations recorded with pervasive sensor networks, SHM aims at extracting meaningful damage-sensitive features from the data, shaped as multivariate time series, and taking real-time decisions concerning the safety level. Within this context, we discuss an approach able to detect and localize a structural damage avoiding any pre-processing of the acquired data. The method takes advantage of the capability of Deep Learning of Fully Convolutional Networks, trained during an offline SHM phase. As a hybrid model- and data-based solution is looked for, Reduced Order Models are also built in the offline phase to reduce the computational burden of the whole monitoring approach. Through a numerical benchmark test, we show how the proposed method can recognize and localize different damage states.
Keywords: structural health monitoring; fully convolutional networks; damage localization; time series analysis; deep learning structural health monitoring; fully convolutional networks; damage localization; time series analysis; deep learning
MDPI and ACS Style

Rosafalco, L.; Corigliano, A.; Manzoni, A.; Mariani, S. A Hybrid Structural Health Monitoring Approach Based on Reduced-Order Modelling and Deep Learning. Proceedings 2020, 42, 67.

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