التفاصيل البيبلوغرافية
| العنوان: |
Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network. |
| المؤلفون: |
Xiong, Chen1 (AUTHOR), Li, Qiangsheng1 (AUTHOR), Lu, Xinzheng1,2 (AUTHOR) luxz@tsinghua.edu.cn |
| المصدر: |
Automation in Construction. Jan2020, Vol. 109, pN.PAG-N.PAG. 1p. |
| مصطلحات موضوعية: |
*EARTHQUAKE resistant design, *ARTIFICIAL neural networks, *ARCHITECTURAL models, *IMAGE segmentation, *DATA management |
| مستخلص: |
A rapid assessment of the seismic damage to buildings can facilitate improved emergency response and timely relief in earthquake-prone areas. In this study, an automated building seismic damage assessment method using an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN) is introduced. The method consists of three parts: (1) data preparation, (2) building image segmentation, and (3) CNN-based building seismic damage assessment. First, a three-dimensional (3D) building model, aerial images, and camera data are used for the following simulation. Next, a building image segmentation method is proposed using the 3D building model as georeference, through which multi-view segmented building images can be obtained. Subsequently, a CNN model based on VGGNet is adopted to assess the seismic damage of each building. The CNN model is fine-tuned based on manually tagged building images obtained from the Internet. Finally, a case study of the old Beichuan town is used to demonstrate the effectiveness of the proposed method. The damage distribution of the area is obtained with an accuracy of 89.39%. • An automated seismic damage assessment framework is proposed for regional buildings. • A building identification method is proposed using 3D building models as georeference. • The CNN is adopted to assess the seismic damage of buildings based on aerial images. • The predicted building damage can be linked to the GIS data for risk management. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |
