1. An Optimal Approach of Adaptive Neuro -Fuzzy Inference System to Predict the Roof Ductility Demand of EBFs Subjected to Near-Fault Pulse-Like Ground Motions
- Author
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Navid Siahpolo, Mehdi Mahdavi Adeli, and Seyed Abdonnabi Razavi
- Subjects
General Computer Science ,Computer science ,General Mathematics ,0211 other engineering and technologies ,global ductility ,020101 civil engineering ,02 engineering and technology ,lcsh:Technology ,0201 civil engineering ,Ductility ,Roof ,performance levels ,021110 strategic, defence & security studies ,Adaptive neuro fuzzy inference system ,lcsh:T ,business.industry ,lcsh:Mathematics ,adaptive neuro-fuzzy inference system ,General Engineering ,Structural engineering ,lcsh:QA1-939 ,General Business, Management and Accounting ,Near fault ,ebf frames ,Pulse (physics) ,business ,intelligent model - Abstract
Careful estimation of global ductility will certainly lead to greater accuracy in the design of structural members. In this paper, a new and optimal intelligent model is proposed to predict the roof ductility (μR) of EBF steel frames exposed to the near-fault pulse-like earthquakes, using the Adaptive Neuro-Fuzzy Inference System (ANFIS). To achieve this goal, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3-,6-, 9-, 12-, 15, 20-stories, steel EBF frames are considered with 3 different types of link beam, column stiffness, and brace slenderness. All models are analysed to reach 4 different performance levels using nonlinear time history under 20 near-fault earthquakes. About 6769 data are applied as ANFIS training data. Subtractive clustering and Fuzzy C-Mean clustering (FCM) methods are applied to generate the purposed model. The results show that FCM provides more accurate outcomes. Moreover, to validate the model, 2257 data are applied (as test data) in order to calculate the correlation coefficient (R) and mean squared error (MSE) between the predicted values of (μR) and the real values. The results of correlation analysis show the high accuracy of the proposed intelligent model.
- Published
- 2020
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