Convolutional Neural Networks for Structural Damage Identification in Assembled Buildings

摘要

This paper investigates the migration learning AlexNet-based algorithm for the recognition of assembly building structures and the recognition based on an improved algorithm, and gives an analysis of the results. The structure of AlexNet convolutional neural network is introduced and the basic principles of migration learning are analysed. The optimal model for the ceiling damage recognition task was obtained through parameter adjustment, with a test accuracy of 96.6%. The maximum improvement in test accuracy is about 4%, with 82.6% and 79.7% for beam and column damage recognition and infill wall damage recognition respectively.