Device and method for predicting earthquake destructive force based on cyclic neural network
A technology of cyclic neural network and prediction device, which is applied in the direction of measuring device, seismology, geophysical measurement, etc., can solve the problems of high efficiency, not satisfying the real-time nature of emergency, and insufficient universality, and achieve the effect of accurate evaluation
Active Publication Date: 2021-04-09
TSINGHUA UNIV
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Problems solved by technology
At present, there are two main ways to obtain earthquake destructive power: one is through field investigation or nonlinear time history analysis, which is accurate but inefficient and does not meet the real-time requirements of emergency; the other is through vulnerability analysis. Analysis, high efficiency but insufficient accuracy and universality
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[0047] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.
[0048] The device and method for predicting earthquake destructive force based on cyclic neural network according to the embodiments of the present invention will be described below with reference to the accompanying drawings.
[0049] First, a device for predicting earthquake destructive force based on a cyclic neural network according to an embodiment of the present invention will be described with reference to the accompanying drawings.
[0050] figure 1 It is a structural schematic diagram of a device for predicting earthqua...
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The invention discloses an earthquake destructive force prediction device and method based on a cyclic neural network, wherein the device comprises: a sensing module for acquiring information of a target object, a calculation and analysis module for providing resource (computing power) support for analysis, The communication module provides information transmission capability, and the display module provides a result display platform; the ground motion data information of the target object is obtained through the sensing module, the ground motion data information is read and preprocessed by the calculation analysis module, and the model is predicted through the neural network. The preprocessed ground motion data information is analyzed to generate an earthquake destructive force prediction result, the communication module sends the earthquake destructive force prediction result to a preset receiving end, and the display module visually converts the earthquake destructive force prediction result, And displayed through the electronic display. Therefore, it is possible to accurately and real-time predict the damage of the target object when it encounters an earthquake, which is of great significance for evacuation organization, earthquake early warning and other work.
Description
technical field [0001] The invention relates to the field of civil structural engineering and the technical field of disaster prevention and reduction, in particular to a device and method for predicting earthquake destructive force based on a cyclic neural network. Background technique [0002] Earthquake disaster is an important security threat faced by buildings and one of the most serious casualties among various natural disasters. It is a factor that must be considered in building design and organizing personnel evacuation. When an earthquake disaster comes, it is of great significance to accurately and timely understand the earthquake damage suffered by the target area for organizing personnel evacuation and rescue and disaster relief. At present, there are two main ways to obtain earthquake destructive power: one is through field investigation or nonlinear time-history analysis, which is accurate but inefficient, and does not meet the real-time requirements of emergen...
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IPC IPC(8): G01V1/30
CPCG01V1/307G01V2210/63
Inventor 陆新征徐永嘉程庆乐
Owner TSINGHUA UNIV