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

A deep neural network and peak acceleration technology, applied in neural learning methods, biological neural network models, seismology, etc., can solve problems such as low accuracy of ground motion prediction, and achieve the effects of good applicability, improved accuracy, and high accuracy

Active Publication Date: 2020-06-09
HARBIN INST OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to solve the technical problem of low precision of earthquake predicti...

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

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specific Embodiment approach 1

[0038] Specific implementation mode one: the method for predicting the peak acceleration of earthquake motion based on the second-order neuron deep neural network in this implementation mode is implemented according to the following steps:

[0039] Step 1: Collect earthquake records and create a data set:

[0040] Select the magnitude (M), projection distance (R JB ), shear wave velocity (V S30 ), region (Region), cover layer thickness (Z 1 ), fault type (Fault Type) and period (T) are the input parameters, and the corresponding peak acceleration of the ground motion is the target parameter, and the values ​​of the input parameter and the target parameter are between -0.5 and 0.5 through the standardization method, and the ground motion data set;

[0041] Step 2: Build a deep neural network with second-order neurons:

[0042] Establish a deep neural network with three hidden layers. The neurons are all second-order elements. The hyperbolic tangent function (Tanh) is used a...

specific Embodiment approach 2

[0059] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the earthquake records in step 1 are selected from the NGA-West2 database.

specific Embodiment approach 3

[0060] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the magnitude (M) in step 1 takes the natural logarithmic value, and the projection distance (R JB ) takes the natural logarithm value.

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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...

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Application Information

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IPC IPC(8): G01V1/30G06K9/62
CPCG01V1/307G06N3/084G06N3/045G06F18/241
Inventor 籍多发李晨曦温卫平翟长海
Owner HARBIN INST OF TECH
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