Equivalent viscous damping for buckling-restrained braced RC frame structures

Structures ◽  
2021 ◽  
Vol 34 ◽  
pp. 1229-1252
Author(s):  
Sina Farahani ◽  
Amir H. Akhaveissy ◽  
Lars Damkilde
Keyword(s):  
Frame Structures ◽  
Viscous Damping ◽  
Rc Frame ◽  
Equivalent Viscous Damping ◽  
Rc Frame Structures

Cumulative damage effects of mainshock-aftershock sequences on RC-frame structures

2019 ◽  
Vol 32 (3-4) ◽  
pp. 157-169
Author(s):  
Lingxin Zhang ◽  
◽  
Baijie Zhu ◽  
Yunqin Xue ◽  
Jialu Ma ◽  
...  
Keyword(s):  
Cumulative Damage ◽  
Frame Structures ◽  
Rc Frame ◽  
Aftershock Sequences ◽  
Rc Frame Structures

Equivalent viscous damping for steel eccentrically braced frame structures with buckling restraint braces

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
E. M. S. D. Jayasooriya ◽  
D. W. U. Indika ◽  
K. K. Wijesundara ◽  
P. Rajeev
Keyword(s):  
Frame Structures ◽  
Viscous Damping ◽  
Equivalent Viscous Damping ◽  
Braced Frame ◽  
Eccentrically Braced Frame

Design Forces for Drift and Damage Control: A Second Look at the Substitute Structure Approach

1994 ◽  
Vol 10 (2) ◽  
pp. 319-331 ◽  
Author(s):  
John F. Bonacci
Keyword(s):  
Reinforced Concrete ◽  
Damage Control ◽  
Dynamic Test ◽  
Frame Structures ◽  
Rc Frame ◽  
Test Results ◽  
Structure Approach ◽  
Maximum Displacement ◽  
Design Loads ◽  
Rc Frame Structures

This paper explores the development of a method that is useful for design of reinforced concrete (RC) frame structures to resist earthquakes. The substitute structure method, originally proposed in the 1970s, makes an analogy between viscously damped linear and hysteretic response for the purpose of estimating maximum displacement. The evolution of the method is retraced in order to emphasize its unique reliance on experimental results, which are needed to establish rules for assignment of substitute linear properties. Recent dynamic test results are used to extend significantly the calibration of the method, which furnishes design loads on the basis of drift and damage control.

A new genetic algorithm-based framework for optimized design of steel-jacketing retrofitting in shear-critical and ductility-critical RC frame structures

2021 ◽  
Vol 243 ◽  
pp. 112684
Author(s):  
Fabio Di Trapani ◽  
Antonio Pio Sberna ◽  
Giuseppe Carlo Marano
Keyword(s):  
Genetic Algorithm ◽  
Frame Structures ◽  
Optimized Design ◽  
Rc Frame ◽  
Rc Frame Structures

Selection of the Most Unfavorable Real Ground Motions for Low- and Mid-rise RC Frame Structures

2013 ◽  
Vol 17 (8) ◽  
pp. 1233-1251 ◽  
Author(s):  
Changhai Zhai ◽  
Zhiwang Chang ◽  
Shuang Li ◽  
Lili Xie
Keyword(s):  
Ground Motions ◽  
Frame Structures ◽  
Rc Frame ◽  
Rc Frame Structures ◽  
Selection Of

EXPECTED ANNUAL LOSS ORIENTED SEISMIC RETROFITTING OPTIMIZATION OF RC FRAME STRUCTURES USING A NEW AI-BASED FRAMEWORK

2021 ◽  
Author(s):  
Fabio Di Trapani ◽  
Antonio Pio Sberna ◽  
Giuseppe Carlo Marano
Keyword(s):  
Seismic Retrofitting ◽  
Frame Structures ◽  
Rc Frame ◽  
Annual Loss ◽  
Rc Frame Structures

scholarly journals Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Athanasios K. Tsaris ◽  
Liborio Cavaleri ◽  
Constantinos C. Repapis ◽  
Angeliki Papalou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fundamental Period ◽  
Critical Parameters ◽  
Frame Structures ◽  
Rc Frame ◽  
Infill Walls ◽  
Rc Structures ◽  
Artificial Neural ◽  
Rc Frame Structures

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

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