Combined LSTM-FC deep network modeling for real-time earthquake hazard simulation of regional building complexes
Building damage often leads to huge losses in earthquake disasters,and it is of great significance to predict the earthquake damage of urban building complexes before and during disasters.Artificial intelligence methods such as traditional BP(back propagation)networks and CNN(convolutional neural networks)networks mostly focus on extracting building information when performing earthquake damage prediction.However,these methods are deficient in processing time-series data of seismic waves,which leads to their limited effectiveness in integrating and analyzing time-series correlations that are crucial for seismic hazard prediction.Therefore,this paper proposes a coupled LSTM(long short-term memory)and FC(fully connected)neural network for earthquake hazard prediction.The LSTM network excels in processing seismic wave information with time-series characteristics,and is able to capture and analyze seismic fluctuation patterns over time.Meanwhile,the Fully Connected network can be used to synthesize and analyze all relevant seismic hazard factors.By quantifying the metrics and determining the input metrics(seismic impact factors)and output metrics(seismic indices)for 265 typical steel-concrete buildings in Yunfu area,the data was trained and optimized by using the LSTM-FC combined deep network,CNN network and BP network model.By comparing the prediction results of the LSTM-FC network model with the elasto-plastic time-range analysis,it is found that the model outperforms the traditional BP and CNN models in terms of fitting effect and accuracy.The fitting effect was improved by 36.8%and 10.6%,and the accuracy improved by 77.6%and 91.7%,respectively,indicating that the LSTM-FC network is more effective in earthquake damage prediction.Meanwhile,the method is applied to the earthquake damage prediction of steel-concrete structural clusters in Yunfu City,Guangdong Province,and the constructed susceptibility matrix is compared with the mean value of the susceptibility matrix in South China,which shows that the error is relatively small,indicating that the model is not only theoretically feasible,but also can show high accuracy and effectiveness in practical applications.
earthquake damage predictionLSTM networkfully connected networksteel and concrete building
NETL
NSTL
万方数据
