Dr. Mohammad Noori, professor of mechanical engineering, and his colleagues have recently published the following journal paper in a special volume of Sensors, a highly ranked journal:
Tianyu Wang, Huile Li, Mohammad Noori*, Ramin Ghiasi, Sin-Chi Kuok and Wael A. Altabey, Probabilistic seismic response prediction of three-dimensional buildings based on Bayesian convolutional neural network, Sensors, 2022, 22, 3775.
Dr. Noori serves as a member of the Sensors Editorial Board and he is the guest editor of the special volume.
*Corresponding Author
Journal Paper Abstract
Seismic response prediction is a challenging problem and is significant in every stage during a structure’s life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures. In this paper, a deep Bayesian convolutional neural network is proposed to predict seismic response. The Bayes-backpropagation algorithm is applied to train the proposed Bayesian deep learning model. A numerical example of a three-dimensional building structure is utilized to validate the performance of the proposed model. The result shows that both acceleration and displacement responses can be predicted with a high level of accuracy by using the proposed method. The main statistical indices of prediction results agree closely with the results from finite element analysis. Furthermore, the influence of random parameters and the robustness of the proposed model are discussed.
