Title :
Seismic damage prediction of multistory building using GIS and Artificial neural network
Author :
Wang, Jun-Jie ; Gao, Hui-Ying ; Liu, Ming-Qiong
Author_Institution :
Environ. Sci. & Eng. Coll., Ocean Univ. of China, Qingdao, China
Abstract :
An integrated GIS and Artificial neural network analysis model for earthquake-damaged, which couples geographic information systems(GIS) with artificial neural networks (ANN) to predict the seismic damage to multistory buildings based on earthquake intensity and adopt the peak acceleration value, is presented here. ANN is used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, and perform spatial analysis on the results. The ANN combined with GIS was found to have a great potential to predict seismic damage.
Keywords :
building; earthquakes; geographic information systems; neural nets; seismology; ANN; artificial neural network; earthquake damage; earthquake intensity; geographic information system; integrated GIS; multistory building; peak acceleration value; seismic damage prediction; spatial analysis; Acceleration; Artificial neural networks; Buildings; Cities and towns; Earthquakes; Geographic Information Systems; Neurons; ANN; Geographic information system (GIS); Seismic prediction; structure vulnerability;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5584603