Deep learning for structural health monitoring under environmental and operational variations

Mohsen Mousavi; Amir H. Gandomi

doi:10.1117/12.2582649

22 March 2021 Deep learning for structural health monitoring under environmental and operational variations

Mohsen Mousavi, Amir H. Gandomi

Proceedings Volume 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV; 115920H (2021) https://doi.org/10.1117/12.2582649
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Abstract

Structural Health Monitoring (SHM) techniques can be classified into global and local monitoring strategies. Regarding the global strategies, the aim is to monitor the structure for any change that can be related to damage globally, whereas, the local damage detection schemes usually aim at detecting damage at a very confined area on the structure. The global techniques, which are also sometimes termed as vibration methods, are very sensitive to Environmental and Operational Variations (EOV). These variations can affect the structural response and, subsequently, mask any changes in vibration signals that can be referred to as damage. It is known that temperature has the most effect on the natural frequencies of any structure. In this paper, an inverse strategy is proposed that aims to predict the temperature variation using frequency time series of the structure. To this end, a Recurrent Neural Networks (RNN) is exploited in which the natural frequency time series obtained from the intact structure along with recorded temperatures are respectively used as training features and label. The trined RNN is then tested on data obtained from the damaged structure. It is shown that the error in predictions increases as the damage occurs. A numerical example is presented to demonstrate the applicability of the proposed method.

Conference Presentation

Citation Download Citation

Mohsen Mousavi and Amir H. Gandomi "Deep learning for structural health monitoring under environmental and operational variations", Proc. SPIE 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, 115920H (22 March 2021); https://doi.org/10.1117/12.2582649

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