Earthquake motion peak acceleration prediction method based on second-order neuron deep neural network

Abstract

The invention discloses an earthquake motion peak acceleration prediction method based on a second-order neuron deep neural network, belongs to the field of earthquake engineering, and aims to solve the technical problem of low precision of earthquake motion prediction. The earthquake motion peak acceleration prediction method comprises the following steps: 1, selecting earthquake magnitudes, projection distances, shear wave velocities, regions, cover layer thicknesses, fault types and periods as input parameters in a data set, and taking corresponding earthquake motion peak accelerations as target parameters; 2, establishing a deep neural network comprising three hidden layers, wherein neurons are second-order elements, a hyperbolic tangent function is adopted as an activation function, amean square error function and an Adam self-adaptive optimization function are adopted for back propagation, and an average absolute error function is adopted as an evaluation function; 3, training adeep neural network model; and 4, peak acceleration prediction. According to the method, a multi-input structure and a second-order neural network are adopted so that the precision of predicting theearthquake motion peak acceleration can be improved, and the applicability of the deep neural network model can be ensured.

Description

technical field [0001] The invention belongs to the field of earthquake engineering, and in particular relates to a method for predicting the peak acceleration of earthquake motion based on a deep neural network of second-order neurons. Background technique [0002] From ancient times to the present, every major earthquake has caused immeasurable losses to the society. In order to reduce and control the losses caused by earthquakes, it is the most important measure to carry out anti-seismic fortification of buildings, and the basis for anti-seismic fortification is earthquake Hazard Analysis. In the seismic hazard analysis, in order to evaluate the seismic intensity through different parameters, it is very important to establish the earthquake motion prediction equation. [0003] At present, the traditional prediction method is mainly to perform empirical regression based on the existing seismic records. This method has good prediction accuracy and uniform distribution of r...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the technical problem of low precision of earthquake prediction, and provide a method for prediction of peak acceleration of earthquake based on second-order neuron deep neural network

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