Blasting peak velocity prediction method and device based on RGA-BPNNG and medium

A GA-BP and peak velocity technology, applied in the field of geotechnical engineering, can solve problems such as large errors and insufficient reliability

Pending Publication Date: 2020-06-12
NANHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a method, device and medium for predicting blasting peak velocity based on RGA-BPNNG to solve the problems ...

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  • Blasting peak velocity prediction method and device based on RGA-BPNNG and medium
  • Blasting peak velocity prediction method and device based on RGA-BPNNG and medium
  • Blasting peak velocity prediction method and device based on RGA-BPNNG and medium

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Embodiment 1

[0061] Such as figure 1 As shown, this embodiment provides a method for predicting peak blasting velocity based on RGA-BPNNG, including:

[0062] Step S01: Obtain blasting characteristic parameters of the area to be blasted;

[0063] Step S02: According to the blasting characteristic parameters and the preset random GA-BP neural network group blasting peak velocity prediction model, predict the blasting peak velocity of the area to be blasted; wherein the preset random GA-BP neural network group blasting The peak velocity prediction model is obtained after training the random GA-BP neural network group through the historical blasting characteristic parameters of the blasted area.

[0064] By obtaining the blasting characteristic parameters of the area to be blasted and the trained random GA-BP neural network group blasting peak velocity prediction model, the blasting peak velocity after blasting in the blasting area can be predicted, so as to reasonably adjust the design blasting cha...

Embodiment 2

[0090] This embodiment provides an RGA-BPNNG-based blasting peak velocity prediction device, including:

[0091] The first data acquisition module is used to acquire the blasting characteristic parameters of the area to be blasted;

[0092] The blasting peak velocity prediction module is used to predict the blasting peak velocity of the area to be blasted based on the blasting characteristic parameters and a preset random GA-BP neural network group blasting peak velocity prediction model; wherein the preset random GA- The BP neural network group blasting peak velocity prediction model is obtained after training the random GA-BP neural network group through the historical blasting characteristic parameters of the blasted area.

[0093] In this embodiment, it also includes:

[0094] The second data acquisition module is used to acquire historical blasting characteristic parameters and blasting peak velocity in the blasted area;

[0095] The sample set generation module is used for standa...

Embodiment 3

[0109] This embodiment provides a computer-readable storage medium, the storage medium stores program instructions, and the program instructions are suitable for a processor to load and execute the RGA-BPNNG-based blasting peak velocity prediction method as described in Embodiment 1. .

[0110] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0111] This application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and co...

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Abstract

The invention discloses a blasting peak speed prediction method and device based on RGA-BPNNG and a medium. The method comprises the steps that blasting characteristic parameters of a to-be-blasted area are acquired; according to the blasting characteristic parameters and a preset random GA-BP neural network group blasting peak speed prediction model, predicting the blasting peak speed of the to-be-blasted area, wherein the preset random GA-BP neural network group blasting peak speed prediction model is obtained by training a random GA-BP neural network group through historical blasting characteristic parameters of a blasted area. The blasting peak speed after blasting in the to-be-blasted area is predicted, blasting characteristic parameters can be reasonably adjusted and designed conveniently, and it is guaranteed that the blasting effect after blasting meets the working condition requirement; according to the method, the blasting peak speed is predicted by the random GA-BP neural network group blasting peak speed prediction model, so that the precision and reliability of the blasting peak speed prediction value are effectively improved.

Description

Technical field [0001] The invention relates to the technical field of geotechnical engineering, in particular to a method, device and medium for predicting peak blasting velocity based on RGA-BPNNG. Background technique [0002] The maturity of engineering blasting technology and its wide application in the field of engineering construction have become an indispensable and important construction method. While bringing huge economic and social benefits, various harmful effects produced by blasting operations also affect the lives and safety of buildings and personnel around the project area. Among them, the impact of blasting vibration is the most significant: building cracks, door and window vibration, and slope landslides are common harmful effects of blasting vibration. Therefore, the demand for accurately predicting the intensity of blasting vibration is becoming more and more urgent. Peak blasting velocity is a direct parameter that reflects the intensity of blasting vibrat...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06K9/62
CPCG06Q10/04G06N3/084G06N3/045G06F18/24323G06F18/214
Inventor 郭钦鹏杨仕教刘迎九相志斌陈然吴彪
Owner NANHUA UNIV
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