Seismic damage assessment of reinforced concrete frames with shear wall according to load pattern

2012 ◽  
Vol 165 (4) ◽  
pp. 199-208 ◽  
Author(s):  
Qmars Yahyazadeh Ahmadi ◽  
Javed Vaseghi Amiri
Keyword(s):  
Reinforced Concrete ◽  
Damage Assessment ◽  
Shear Wall ◽  
Seismic Damage ◽  
Concrete Frames ◽  
Reinforced Concrete Frames ◽  
Load Pattern ◽  
Seismic Damage Assessment

Seismic Damage in Reinforced Concrete Frames

1981 ◽  
Vol 107 (9) ◽  
pp. 1713-1729 ◽  
Author(s):  
Hooshang Banon ◽  
John M. Biggs ◽  
H. Max Irvine
Keyword(s):  
Reinforced Concrete ◽  
Seismic Damage ◽  
Concrete Frames ◽  
Reinforced Concrete Frames

scholarly journals Multiple-Input Convolutional Neural Network Model for Large-Scale Seismic Damage Assessment of Reinforced Concrete Frame Buildings

2021 ◽  
Vol 11 (17) ◽  
pp. 8258
Author(s):  
Chen Xiong ◽  
Jie Zheng ◽  
Liangjin Xu ◽  
Chengyu Cen ◽  
Ruihao Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Convolutional Neural Network ◽  
Damage Assessment ◽  
Prediction Accuracy ◽  
Seismic Damage ◽  
Computation Efficiency ◽  
Frame Buildings ◽  
Multiple Input ◽  
Seismic Damage Assessment

This study introduces a multiple-input convolutional neural network (MI-CNN) model for the seismic damage assessment of regional buildings. First, ground motion sequences together with building attribute data are adopted as inputs of the proposed MI-CNN model. Second, the prediction accuracy of MI-CNN model is discussed comprehensively for different scenarios. The overall prediction accuracy is 79.7%, and the prediction accuracies for all scenarios are above 77%, indicating a good prediction performance of the proposed method. The computation efficiency of the proposed method is 340 times faster than that of the nonlinear multi-degree-of-freedom shear model using time history analysis. Third, a case study is conducted for reinforced concrete (RC) frame buildings in Shenzhen city, and two seismic scenarios (i.e., M6.5 and M7.5) are studied for the area. The simulation results of the area indicate a good agreement between the MI-CNN model and the benchmark model. The outcomes of this study are expected to provide a useful reference for timely emergency response and disaster relief after earthquakes.

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