en Applications Applications پژوهشي Research <span style="font-size:11.5pt"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times="">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 (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-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 5- and 10-story steel MRFs compared to other neural network models.</span></span></span> seismic performance level, steel moment resisting frame, neural network, feed-forward back-propagation 275 293 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-405&slc_lang=en&sid=1 Z.H.F. Jafar 180031947532846002511 180031947532846002511 No Department of Civil Engineering, Urmia University, Urmia, Iran S. Gholizadeh s.gholizadeh@urmia.ac.ir 180031947532846002512 180031947532846002512 Yes Department of Civil Engineering, Urmia University, Urmia, Iran