Online structural health monitoring by model order reduction and deep learning algorithms. (15th October 2021)

• Record Type: Journal Article
• Authors: Rosafalco, Luca; Torzoni, Matteo; Manzoni, Andrea; Mariani, Stefano; Corigliano, Alberto
• Is Part Of: Computers & structures. Volume 255(2021)
• Publication Date: 2021-10-15
• Subjects: Structural health monitoring -- Deep learning -- Reduced order models -- Fully convolutional networks -- Damage localization; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106604
• Languages: English
• ISSNs: 0045-7949

Abstract

Abstract: Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines parametric Model Order Reduction (MOR) techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration measurements recorded on the monitored structure. First, a dataset of possible structural responses under varying operational conditions is built through a physics-based model, allowing for a finite set of predefined damage scenarios. Then, the dataset is used for the offline training of the FCN. Because of the extremely large number of model evaluations required by the dataset construction, MOR techniques are employed to reduce the computational burden. The trained classifier is shown to be able to map unseen vibrational recordings, e.g. collected on-the-fly from sensors placed on the structure, to the actual damage state, thus providing information concerning the presence and also the location of damage. The proposed strategy has been validated by means of two case studies, concerning a 2D portal frame and a 3D portal frame railway bridge; MOR techniques have allowed us to respectively speed up the analyses about 30 and 420 times. For both the case studies, after training the classifier has attained an accuracy larger than 85 % .