Research on Prediction of Site Equivalent Shear Wave Velocity Change Based on BP Neural Network
In this paper,a total of 30952 records from 407 stations of the Japanese KiK-net is used to propose a prediction model for the change of equivalent shear wave velocity ratio based on BP neural network.The model adopts the mean square error function and the Adam optimization algorithm,consists of three inputs,five hidden neurons and one output.The input parameters are Peak Ground Acceleration(PGA),Arias intensity(Ia)and site VS30.The output parameter is site equivalent shear wave velocity ratio(Vs).The research shows that the residual error of the network model is unbiased for each input variable,and has good prediction performance in many kinds of sites.Compared with the function curve of the traditional least-square method,the neural network model has a relatively better per-formance.In the prediction curve of the network model,the shear wave velocity of the site of Class B decreases by 5％when the PGA reaches about 175 cm/s2,and the shear wave velocity of the sites of Class D and E decreases by 5％when the PGA reaches about 140 cm/s2.The nonlinear threshold of most sites is between 50～100 cm/s2.PGA occupies a high weight in the network model and is the main controlling parameter of the site equivalent shear wave velocity change.The network model captures that the equivalent shear wave velocity ratio of the site has a downward trend with the increase of PGA.At the same time,it shows that the Class D and E sites are greatly affected by PGA,and the declineing range is larger.
万方数据
维普
