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.
Highlights
The ductility concept is well-defined in various performance levels with developing numerical techniques and the tendency of seismic codes to apply ductile structures
The main emphasis was on introducing the capability of the proposed model to fit into the framework of design methods based on a simple elastic analysis
The produced intelligent model was a nonlinear function of the number of stories, brace slenderness, column stiffness, a basic period of the structure, link beam length to the total length of the beam ratio, design performance level, and behaviour factor of the structure
Summary
The ductility concept (demand and capacity) is well-defined in various performance levels with developing numerical techniques and the tendency of seismic codes to apply ductile structures. Techniques that work on a particular model are capable of analysing complex nonlinear and time-varying problems, they face some limitations Combining these with other issues like decision making has inspired the development of intelligent techniques, including fuzzy logic, genetic algorithms, neural networks, and expert systems. Neural network technology can be applied to learn system behaviour based on input and output data ( Straccia, 2013) This knowledge may be applied to create fuzzy rules and membership functions, reducing development time. Columns, and beam segments in link outside are modelled to stay basically elastic on the basis of capacity design concepts (Özhendekci and Özhendekci, 2008; Kuşyılmaz and Topkaya, 2015) Such members need, have sufficient strength to resist forces relative to the link expected strength, such as strain hardening (Fakhraddini et al, 2019). In order that the frame first to third modes are defined by an equivalent viscous damping factor of 0.05, stiffness and mass coefficients are determined
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Mathematical, Engineering and Management Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
