Rapid Structural Safety Assessment Using a Deep Neural Network

Abstract

ABSTRACT A novel assessment method for rapid structural safety state assessment is developed based on state-of-the-art machine learning technology. In this paper, a deep neural network (DNN), which originated in computer science, is first introduced, including its concept, structure and related training algorithms. Then the evaluation procedure to determine the structural safety state based on the DNN is developed. In the procedure, the pseudo-acceleration spectra and the structural safety states are selected as the input and output of the DNN model, respectively. Finally, the procedure of the novel assessment method is illustrated using a five-story reinforced concrete (RC) frame as an example. The effectiveness and accuracy are validated, and the influence of the number of hidden layers and size of the training data on the performance of the DDN is also investigated. The results demonstrate that the proposed method can evaluate the structural safety state rapidly based on the DNN. The most appropriate number of hidden layers is two considering both training accuracy and test accuracy. The training accuracy and test accuracy were 93.45% and 93.14%, respectively, using the two-hidden-layer DNN model trained on a training dataset of size 66,612.