Risk Evaluation of Water Inrush from Coal Floor Based on BP Neural Network

2015 ◽  
Vol 744-746 ◽  
pp. 1728-1732 ◽  
Author(s):  
Wei Tao Liu ◽  
Shi Liang Liu ◽  
Yan Shuang Sun
Keyword(s):  
Neural Network ◽  
Coal Seam ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Evaluation Method ◽  
Water Inrush ◽  
Test Collection ◽  
Working Face ◽  
Floor Water Inrush

According to the nonlinear dynamic characteristic of coal seam floor water inrush, coal seam floor water inrush risk evaluation which includes 4 first level indicators,14 level two indexes was built based BP neural network. According to the test collection of engineering data, coal seam floor water inrush risk evaluation system based VB and MATLAB is reliable. Application to a mine coal seam No.2 working face was verified. The results show that, the evaluation method in water inrush is feasible, reasonable.

scholarly journals A BP Neural Network-Based Hierarchical Investment Risk Evaluation Method Considering the Uncertainty and Coupling for the Power Grid

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 110279-110289
Author(s):  
Shujuan Chen ◽  
Qin Jiang ◽  
Yuqing He ◽  
Ruanming Huang ◽  
Jiayong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Power Grid ◽  
Investment Risk

scholarly journals Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Changwei ◽  
Li Zonghao ◽  
Guo Xueyan ◽  
Yu Wenying ◽  
Jin Jing ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Construction Project ◽  
Network Models ◽  
Training Algorithms ◽  
Neural Network Models ◽  
Railway Construction ◽  
Railway Construction Project

Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

scholarly journals A New Quantitative Method for Risk Assessment of Coal Floor Water Inrush Based on PSR Theory and Extension Cloud Model

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingyou Jiang ◽  
Bo Ren ◽  
Mingqing Su ◽  
Bao Wang ◽  
Xin Li ◽  
...  
Keyword(s):  
Coal Seam ◽  
Cloud Model ◽  
Evaluation Model ◽  
Water Inrush ◽  
Indicator System ◽  
Working Face ◽  
Actual Evaluation ◽  
Evaluation Indicator System ◽  
Floor Water Inrush ◽  
Cloud Theory

In order to scientifically and reasonably assess the risk of water inrush from the coal seam floor, considering the influence of natural environmental factors such as hydrogeology, mining, and human intervention, the PSR model of ecosystem health evaluation was introduced, and the risk evaluation indicator system of water inrush from the coal seam floor was established. In order to solve the randomness and fuzziness of water inrush event evaluation, the evaluation model is constructed based on extension cloud theory and is applied in the 12123 working face of Pan Er coal mine of Huainan Mining Group. The application results show that the evaluation results are basically consistent with the actual situation, which shows that the model can be used in the actual evaluation work and is scientific.

Application of Vulnerability Index Method in Floor Water Inrush Evaluation

2014 ◽  
Vol 614 ◽  
pp. 321-326 ◽  
Author(s):  
Xiao Ming Li ◽  
Qi Mou Zhou ◽  
Xiao Rui Xiang ◽  
Jun Liu
Keyword(s):  
Coal Seam ◽  
Evaluation Method ◽  
Water Inrush ◽  
Vulnerability Index ◽  
Assessment Method ◽  
Coal Industry ◽  
Index Method ◽  
Vulnerability Index Method ◽  
Floor Water Inrush ◽  
The Impact

There are many factors that promote the coal seam floor water bursting, one coal seam floor water inrush evaluation method that can truly reflect being controlled by the impact of multiple factors and having very complex mechanism and process of evolution in the current is very necessary. In this paper, the case of the 9# coal seam in Shanxi Liulin Hongshengjude coal industry Co. Ltd.is taken as an example, analyzing the application of vulnerability index method in coal seam floor water inrush evaluation, a comparison is made on the assessment results obtained from vulnerability index method and the traditional water inrush coefficient assessment method. The results indicate that the assessment result of the vulnerability index method which considers factors comprehensively is truer to the reality and more advantages.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

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