دوره 14، شماره 2 - ( 11-1402 )
جلد 14 شماره 2 صفحات 293-275 |
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چکیده: (2015 مشاهده)
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.
نوع مطالعه: پژوهشي |
موضوع مقاله:
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دریافت: 1403/1/22 | پذیرش: 1403/2/31 | انتشار: 1403/3/7
دریافت: 1403/1/22 | پذیرش: 1403/2/31 | انتشار: 1403/3/7
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