CNN-based model updating for structures by direct use of dynamic structural response measurements

Abstract

This study presents a convolutional neural network (CNN)-based model updating method for structures using dynamic structural responses. In the presented method, the dynamic structural response is used directly for model updating, thereby omitting the manual process of extracting modal parameters. In the method, the type of stiffness variable in the target structure for model updating is selected, and a number of candidate models are generated by multiple selected stiffness variables in the structures and possible values of variables. Dynamic structural responses are then extracted by time history structural analysis of the candidate models, and a CNN sets dynamic structural response as input, and information on stiffness variables as output is introduced to learn the relationship between the two data. CNNs trained using dynamic displacement response and dynamic acceleration response are presented as Model_XT and Model_XTa, respectively. The presented models can rapidly estimate stiffness of the target structure by entering dynamic responses into the models. These models are applied to model updating for three example structures, and the stiffness estimation performance for model updating is evaluated. Furthermore, the length of data used for CNN training, the stiffness estimation performance according to the CNN input layer settings, and the stiffness estimation applied with limited data to the updated model are analyzed.