Multi-platform modelling of masonry infilled frames

2021 ◽  
Vol 43 ◽  
pp. 102561
Author(s):  
Alex Brodsky ◽  
Xu Huang
2018 ◽  
Vol 14 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Farhad Akhoundi ◽  
Graça Vasconcelos ◽  
Paulo Lourenço

2020 ◽  
Vol 32 ◽  
pp. 101683
Author(s):  
Mohammad Yekrangnia ◽  
Panagiotis G. Asteris

Buildings ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 121 ◽  
Author(s):  
Tanja Kalman Šipoš ◽  
Kristina Strukar

In order to test the reliability of neural networks for the prediction of the behaviour of multi-storey multi-bay infilled frames, neural network processing was done on an experimental database of one-storey one-bay reinforced-concrete (RC) frames with masonry infills. From the obtained results it is demonstrated that they are acceptable for the prediction of base shear (BS) and inter-storey drift ratios (IDR) in characteristic points of the primary curve of infilled frame behaviour under seismic loads. The results obtained on one-storey one-bay infilled frames was extended to multi-bay infilled frames by evaluating and comparing numerical finite element modelling(FEM) modelling and neural network results with suggested approximating equations for the definition of bilinear capacity by defined BS and IDRs. The main goal of this paper is to offer an interpretation of the behaviour of multi-storey multi-bay masonry infilled frames according to a bilinear capacity curve, and to present the infilled frame’s response according to the contributions of frame and infill. The presented methodology is validated by experimental results from multi-storey multi-bay masonry infilled frames.


Sign in / Sign up

Export Citation Format

Share Document