scholarly journals Building Damage Assessment Using Scenario Based Tsunami Numerical Analysis and Fragility Curves

Water ◽  
2016 ◽  
Vol 8 (3) ◽  
pp. 109 ◽  
Author(s):  
Khawar Rehman ◽  
Yong-Sik Cho
Keyword(s):  
Numerical Analysis ◽  
Damage Assessment ◽  
Fragility Curves ◽  
Building Damage

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

scholarly journals A simplified early-warning system for imminent landslide prediction based on failure index fragility curves developed through numerical analysis

2015 ◽  
Vol 7 (4) ◽  
pp. 1406-1425 ◽  
Author(s):  
Ugur Ozturk ◽  
Yalelet Alemnew Tarakegn ◽  
Laura Longoni ◽  
Davide Brambilla ◽  
Monica Papini ◽  
...  
Keyword(s):  
Numerical Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Fragility Curves ◽  
Warning System ◽  
Landslide Prediction ◽  
Failure Index
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