An improved nonlinear dynamic reduction method for complex jointed structures with local hysteresis model

2021 ◽  
Vol 149 ◽  
pp. 107214
Author(s):  
Dong Wang
2018 ◽  
Vol 15 (3) ◽  
pp. 672-684 ◽  
Author(s):  
Guo-qing Chen ◽  
Run-qiu Huang ◽  
Feng-shou Zhang ◽  
Zhen-fei Zhu ◽  
Yu-chuan Shi ◽  
...  

AIAA Journal ◽  
2011 ◽  
Vol 49 (10) ◽  
pp. 2295-2304 ◽  
Author(s):  
Paolo Tiso ◽  
Eelco Jansen ◽  
Mostafa Abdalla

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 524
Author(s):  
Joo-Ki Son ◽  
Chang-Hwan Lee

Various seismic analysis methods are being used to predict the response of structures to earthquakes. Although nonlinear dynamic analysis (NDA) is considered an ideal method to represent the most realistic behavior of a structure among these various methods, correct results can be derived only when the analysis model is carefully developed by a knowledgeable person. It is particularly important to properly implement the behavior characteristics depending on the reversed cyclic load in the NDA of a building made of reinforced concrete (RC) moment frames. This study evaluated the hysteresis model suitable for NDA of existing RC moment frames, and 45 analysis models were reviewed, in which the pivot, concrete, and Takeda hysteresis models were applied differently to beams and columns. The pivot model was evaluated as the most reliable hysteresis model for each structural member by comparing and analyzing not only the responses of the entire frame but also the responses of column and beam members focusing on energy dissipation. However, this model can have practical limitations in that the parameters associated with the reinforcement detailing and applied loads need to be defined in detail. The analysis model applying Takeda to the beam, which predicted the average response at a reliable level compared to the reference model, was identified as a practical alternative when it is difficult to apply the pivot model to all frame members.


2014 ◽  
Vol 115 ◽  
pp. 102-110 ◽  
Author(s):  
Sajjad Zadkhast ◽  
Juri Jatskevich ◽  
Ebrahim Vaahedi ◽  
Arash Alimardani

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