Prediction of Instability and Mechanism of Multi-Factor Comprehensive Action on Mine Goaf

2014 ◽  
Vol 13 ◽  
pp. 39-48 ◽  
Author(s):  
Wei Jian Yu ◽  
Shao Hua Du ◽  
Wei Jun Wang
Keyword(s):  
Bp Neural Network ◽  
Engineering Geology ◽  
Influence Factors ◽  
Surrounding Rock ◽  
Engineering Properties ◽  
Engineering System ◽  
Iron Mine ◽  
Actual Change ◽  
Real Time Tracking ◽  
Function Matrix

In allusion to phenomenon of large instability of underground goaf, research is carried on prediction of instability and mechanism of multi-factor comprehensive action. Firstly, according to large number of examples of instability disaster in mines at home and abroad, influence factors of goaf stability was analyzed, including characteristics of surrounding rock, conditions of engineering geology, engineering properties, structure parameters of goaf and other artificial mining and time etc; then, using BP neural network to establish the comprehensive function matrix RSE, and putting forward a prediction index Fi, this index can be real-time tracking on the actual change conditions of surrounding rock scene in mining goaf, showing the comprehensive action results of each factor on steady state of underground goaf engineering system; finally, on the basis of 36 samples, the stable value and interval value of the corresponding multi-factor comprehensive action index Fi can be obtained after calculation by using comprehensive function matrix RSE. An actual example showed that, value of multi-factor comprehensive action index Fi was-0.8159 in goaf of an iron mine, which was in instability state.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network

2014 ◽  
Vol 1003 ◽  
pp. 226-229 ◽  
Author(s):  
Ying Hong Xie ◽  
Xiao Wei Han ◽  
Qi Li
Keyword(s):  
Neural Network ◽  
Information Technology ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Evolution Model ◽  
Good Reliability ◽  
Bp Neural Network Model ◽  
Near Future ◽  
Xi’An City

In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO2,NO2,PM10, CO,O3 (in one hour ) and O3 (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

Time Series Forecasting of Tunnel Surrounding Rock Displacement

2011 ◽  
Vol 261-263 ◽  
pp. 1789-1793 ◽  
Author(s):  
Guang Xiang Mao ◽  
Yuan You Xia ◽  
Ling Wei Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Stress ◽  
Rock Types ◽  
Network Time ◽  
Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Study on Optimization of Numerical Simulation for Smooth Blasting Parameters in Xishimen Iron Mine

2014 ◽  
Vol 529 ◽  
pp. 605-610 ◽  
Author(s):  
Jian Dong Qi ◽  
Pei Wang ◽  
Yong Tao Gao ◽  
Zhong An Jiang
Keyword(s):  
Numerical Simulation ◽  
Mechanical Property ◽  
Finite Element ◽  
Rock Avalanche ◽  
Simulation Software ◽  
Surrounding Rock ◽  
Iron Mine ◽  
Finite Element Numerical Simulation ◽  
Smooth Blasting

In order to make normal rock avalanche but not have significant impact on other parts of surrounding rock and produce evident deformation in the process of blasting heading, the basic mechanical property of rocks in Xishimen Iron Mine was taken as example to analyze. The process of coupling charge blasting in the surrounding holes of smooth blasting was simulated by applying the finite element numerical simulation software ANSYS/ LS-DYNA. The result shows that the surrounding rock has smallest stress and spall normally when borehole spacing of surrounding holes is 0.6m, thus boosting the efficiency of smooth blasting.

BP Neural Network Model on Choice of Project Manager for Highway Slope Treatment

2014 ◽  
Vol 505-506 ◽  
pp. 274-277
Author(s):  
Bin Wang ◽  
Yong Tao Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Theory ◽  
Influence Factors ◽  
Project Manager ◽  
Slope Treatment ◽  
Neural Network Theory ◽  
Bp Neural Network Model

To get the quantified indexes of comprehensive capacity about project manager, based on the modal on artificial neural network theory, different influence factors about choice of project manager for highway slope treatment were analyzed , identified, quantified and evaluated , then comprehensive capacity of the manager were analyzed. Such procedure provided a new method for choice of project manager for highway slope treatment.

Study on the Deformation Forecasting for the Tunnel Surrounding Rock Using the Neural Network

2013 ◽  
Vol 353-356 ◽  
pp. 828-832
Author(s):  
Guo Feng Wang ◽  
Wen Zhao ◽  
Yong Ping Guan ◽  
Lei Liang
Keyword(s):  
Neural Network ◽  
Maximum Error ◽  
Mining Area ◽  
Surrounding Rock ◽  
Rock Deformation ◽  
The Neural Network ◽  
Sublevel Caving ◽  
Iron Mine ◽  
Set Up ◽  
Surrounding Rock Deformation

The non-pillar sublevel caving method is used in Iron Mine in Banshi. In the mining area, there are many folds and faults, the inclination of ore body changes greatly, and ore and rock are fragmentized. The tunnel often collapsed and the surrounding rock deformation was getting large during the construction stage. Using the data of tunnel surrounding rock deformation, we adopt the neural network method to set up the mapping relation between the tunnel surrounding rock deformation and the project factors, including tunnel deepness, tunnel dimension, measuring time and surrounding rock quality. The analyzing results show that the maximum error between the forecast and the testing data is 13%, which indicates that this method is useful and feasible to the mining engineering. Key words: rock pressure; measure, deformation of the tunnel surrounding rock; neural network; data normalization; mapping

scholarly journals Study and Application on Stability Classification of Tunnel Surrounding Rock Based on Uncertainty Measure Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Hujun He ◽  
Yumei Yan ◽  
Cuixia Qu ◽  
Yue Fan
Keyword(s):  
Measure Theory ◽  
Influence Factors ◽  
Surrounding Rock ◽  
Uncertainty Measure ◽  
In Situ Data ◽  
Rock Stability ◽  
Practical Engineering ◽  
Stability Classification ◽  
Recognition Criterion

Based on uncertainty measure theory, a stability classification and order-arranging model of surrounding rock was established. Considering the practical engineering geologic condition, 5 factors that influence surrounding rock stability were taken into account and uncertainty measure function was obtained based on the in situ data. In this model, uncertainty influence factors were analyzed quantitatively and qualitatively based on the real situation; the weight of index was given based on information entropy theory; surrounding rock stability level was judged based on credible degree recognition criterion; and surrounding rock was ordered based on order-arranging criterion. Furthermore, this model was employed to evaluate 5 sections surrounding rock in Dongshan tunnel of Huainan. The results show that uncertainty measure method is reasonable and can have significance for surrounding rock stability evaluation in the future.

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