دوره 14، شماره 4 - ( 7-1403 )
جلد 14 شماره 4 صفحات 645-629 |
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چکیده: (5272 مشاهده)
This paper employs neural network models to assess the seismic confidence levels at various performance levels, as well as the seismic collapse capacity of steel moment-resisting frame structures. Two types of shallow neural network models including back-propagation (BP) and radial basis (RB) models are utilized to evaluate the seismic responses. Both neural network models consist of a single hidden layer with a different number of neurons. The prediction accuracy of the trained neural network models is compared using two illustrative examples of 6- and 12-story steel moment-resisting frames. The obtained numerical results indicate that the BP model outperforms the RB model in predicting seismic responses.
نوع مطالعه: پژوهشي |
موضوع مقاله:
Applications
دریافت: 1403/8/21 | پذیرش: 1403/10/5 | انتشار: 1403/7/25
دریافت: 1403/8/21 | پذیرش: 1403/10/5 | انتشار: 1403/7/25
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