Study on the Prediction of Ground Motion Amplitude Based on Deep Neural Network
The ground motion prediction model is a critical component in disaster analysis and structural design.In recent years,neur-al network technology has increasingly been applied to develop such prediction models.However,most existing models are single-lay-er fully connected neural networks that use the equivalent shear wave velocity of soil layers within 30 meters below the surface(VS30)as the site input parameter.This approach often overlooks the impact of the complete soil layer thickness and shear wave velocity informa-tion on ground motion amplitude.In this paper,a combined convolutional neural network(CNN)and fully connected neural network model is proposed.A dataset comprising 39192 ground motion records from 3174 earthquakes recorded by KiK-net is used to build a ground motion amplitude prediction model based on deep neural network technology.The model's input parameters include the mag-nitude,source depth,epicenter distance,thickness of each soil layer,shear wave velocity information,and underground ground motion amplitude.The output is the corresponding ground motion amplitude(PGA,PGV,or PGD).The model is trained and its evaluation in-dexes are calculated.The results indicate that:(1)The coefficient of determination(R2)of the mixed input neural network model ex-ceeds 0.85.The model residual follows a normal distribution,and the mean residual is close to 0,demonstrating an unbiased character-istic.(2)Compared to the empirical formula,the prediction accuracy of the hybrid network model improves by 26.9％,16.5％,and 11.6％for PGA,PGV,and PGD,respectively.(3)Compared to the traditional fully connected neural network model using VS30 as the site parameter,the Pearson correlation coefficient and various evaluation indexes of the predicted values and actual values show im-provement.The mean and standard deviation of the model residual are smaller,and the prediction accuracy of the PGA,PGV,and PGD models improves by about 6.3％,3.9％,and 3.4％,respectively.This combined neural network model can better predict seismic amp-litude,taking into account the complete soil layer information.
万方数据
维普
