Identification of the damage location for the structural sealant based on deep learning

Abstract

Structural sealants are essential to maintain the safety of panel units of hidden frame glass curtain wall. Damage localization of the concealed structural sealant is still difficult because of the unknown baseline model. In this paper, a convolutional neural network-based identification method is proposed to localize the damage of structural sealants without the baseline model. The method develops a novel input of the convolutional neural network (CNN), multi-symmetry-point images (MSPI), which is encoded by the response of four symmetrical points to impulse excitations. The CNN would identify the damage location by discerning differences among four images. Then, a dataset with 28803 samples, which considers the effect of the multiple damages and noise, was used to train the CNN. Three types of transformed images and four CNN models were compared to optimize the input signals and the configuration of the CNN. A series of numerical examples indicated that the Gram angle difference field is the optimal image transformation method, and improved-DenseNet121 is the optimal CNN for damage localization in structural sealants. Then, several laboratory experiments validate the effectiveness of the optimized image input and CNN with an accuracy rate of 92 % in the identification of damage location.