Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks

2018 ◽  
Vol 165 ◽  
pp. 120-141 ◽  
Author(s):  
Konstantinos Morfidis ◽  
Konstantinos Kostinakis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seismic Damage ◽  
Damage Prediction ◽  
Artificial Neural

Real-time Seismic Damage Detection of Concrete Shear Walls Using Artificial Neural Networks

2012 ◽  
Vol 17 (1) ◽  
pp. 137-154 ◽  
Author(s):  
Mohammadreza Vafaei ◽  
Azlan bin Adnan ◽  
Ahmad Baharuddin Abd. Rahman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Detection ◽  
Real Time ◽  
Shear Walls ◽  
Seismic Damage ◽  
Artificial Neural

scholarly journals Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Hanxi Jia ◽  
Junqi Lin ◽  
Jinlong Liu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Rapid Assessment ◽  
Seismic Damage ◽  
Assessment Model ◽  
Random Forest Algorithm ◽  
Importance Value ◽  
Damage States ◽  
Artificial Neural

Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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