Rock aging deformation prediction method and system based on LSTM deep learning

A technology of deep learning and prediction methods, applied in special data processing applications, instruments, biological neural network models, etc., can solve the problems that experimental data cannot be reflected, data analysis is not in-depth, and the cost is high, and achieve accurate prediction, high cost, and high cost. The effect of improving accuracy

Active Publication Date: 2020-10-30
POWER CHINA KUNMING ENG CORP LTD +1
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] This application provides a rock time-dependent deformation prediction method and system based on LSTM deep learning to solve the above technical problems. This method is used to solve the existing rock rheological test methods. Simple processing; experimental data cannot reflect the situation after long-term use; technical problems with high cost

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Rock aging deformation prediction method and system based on LSTM deep learning
  • Rock aging deformation prediction method and system based on LSTM deep learning
  • Rock aging deformation prediction method and system based on LSTM deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0068] The present application is described in detail below in conjunction with the examples, but the present application is not limited to these examples.

[0069] see figure 1 , the method for predicting rock time-dependent deformation based on LSTM deep learning provided by this application includes the following steps:

[0070] Step S100: Obtain the existing data of rock deformation over time;

[0071] Preferably, the existing data sources are at least one of: engineering test reports, on-site monitoring, data obtained by processing measured rheological curves, and data obtained by indoor rheological tests on self-made samples. Afterwards, all the existing rheological experimental data acquired are transferred through the Python program for analysis. Existing data in this application refers to rheological data obtained through existing experimental means.

[0072] Step S200: After cleaning the existing data, divide the data set to obtain training set, verification set a...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a rock aging deformation prediction method and system based on LSTM deep learning. The rock aging deformation prediction method comprises the following steps: S100, obtaining existing data of rock aging deformation; S200, after existing data is cleaned, carrying out the data set division to obtain a training set, a verification set and a test set; S300, establishing a timesequence network prediction model; S400, inputting the verification set and the test set into the time sequence network prediction training model, performing testing to obtain a test result, optimizing the model according to the test result, increasing the training speed of the optimization model, and performing hyper-parameter debugging to obtain a rock aging deformation prediction model; S500, inputting actual measurement parameters into the rock aging deformation prediction model to obtain a rock aging deformation prediction result. By processing existing data such as an indoor rock rheological test, effective and accurate prediction of a rock aging deformation result is realized.

Description

technical field [0001] The application relates to a rock time-dependent deformation prediction method and system based on LSTM deep learning, which belongs to the field of rock time-dependent deformation prediction methods in geotechnical engineering and geological disaster prevention and control. Background technique [0002] The essence of rheological characteristics of rock is the time effect of its stress and strain. In general, rheological studies mainly include creep, stress relaxation, long-term strength, elastic aftereffects and hysteresis effects (viscosity effects). Rock creep refers to the process in which the stress remains constant and the deformation increases with time, so it is most closely related to engineering and has received special attention from scholars. [0003] The rheological characteristics of rock are closely related to the long-term stability and safety of rock engineering, such as slope engineering, tunnel engineering, nuclear waste storage, h...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F16/215G06F16/2458G06N3/04
CPCG06F30/27G06F16/215G06F16/2465G06N3/049G06N3/048G06N3/044Y02T90/00
Inventor 宁宇徐伟徐卫亚朱国金孟庆祥杨小龙黄青富
Owner POWER CHINA KUNMING ENG CORP LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap