NEURAL NETWORK-BASED EVALUATION OF SEISMIC RESPONSE OF STEEL MOMENT FRAMES

The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (۵, ۱۰, ۱۵, ۲۰, and ۵۰) were trained to predict the maximum inter-story drift ratios of ۵- and ۱۰-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both ۵- and ۱۰-story steel MRFs compared to other neural network models.

Keywords:

seismic performance level , steel moment resisting frame , neural network , feed-forward back-propagation