Prediction of Target Displacement of Reinforced Concrete Frames Using Artificial Neural Networks

Abstract

In this paper, the application of artificial neural network (ANN) in predicting seismic response of reinforced concrete (RC) frames with masonry infilled walls is investigated. The objective of this research is to predict roof displacement and base shear (ANN outputs) in the target displacement. The total of 855 database were prepared for modeling neural network using finite element method (FEM) by changing six parameters (the input parameters of ANN) including the number of bays, the number of stories, thickness of masonry infill, infilled wall ratio, existence of soft story and design spectral acceleration. A training set of 513 prepared database were used as training data and the validation set of 342 database were used as validation data in the next step. In the present study, two ANNs were trained; a multilayer perseptron (MLP) with Levenberg–Marquardt (LM) back propagation algorithms and a Radial Basis function (RBF), both with different structures and the best structure for each of them was obtained. The performance of ANNs was evaluated using mean square error (MSE) and correlation coefficient (R2) criteria. Results indicate that using both MLP and RBF ANNs for predicting target displacement have been appropriate and have low error as well as high speed. Furthermore, RBF network has a higher speed in training process of data compared to MLP network.