Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings

Abstract

An accurate response prediction model is of great importance in various applications such as damage detection, structural health monitoring, and vibration control. Development of such a methodology for large civil structures is challenging because of their size and complicated behavior and noise-contaminated, nonlinear, and nonstationary nature of the signals. In addition, the prediction model must have a low computational burden for real-time applications. In this article, a new methodology and a nonlinear autoregressive exogenous model (NARX)-based recurrent neural network (NN) model is presented for accurate response prediction of large structures. The methodology is based on adroit integration of three concepts: a recent signal processing concept, empirical mode decomposition (EMD), mutual information (MI) index from the information theory, and a probabilistic Bayesian-based training algorithm. The EMD method is used to remove the noise in the measured signals. An MI index is proposed to determine the optimum number of neurons in the hidden layer of the NN model with the goal of reducing the computational requirements without affecting its performance. Finally, Bayesian regularization (BR) is proposed to train the optimized NN model. The effectiveness of the proposed methodology is assessed by predicting the structural response of a 1:20-scaled 38-story highrise building structure subjected to seismic excitations and ambient vibrations, and a five-story steel frame subjected to different levels of the Kobe earthquake.