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Prediction and Evaluation of Rockburst Based on Depth Neural Network
- Source :
- Advances in Civil Engineering, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.
- Subjects :
- Difficult problem
Article Subject
Artificial neural network
Computer simulation
business.industry
0211 other engineering and technologies
02 engineering and technology
Structural engineering
Engineering (General). Civil engineering (General)
010502 geochemistry & geophysics
01 natural sciences
Construction site safety
Stress (mechanics)
Rock burst
Compressive strength
TA1-2040
business
Geology
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Civil and Structural Engineering
Test data
Subjects
Details
- ISSN :
- 16878094 and 16878086
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Advances in Civil Engineering
- Accession number :
- edsair.doi.dedup.....1a56e15223ece890d0225bf8ced87590