scholarly journals Evaluation of dump slope stability of a coal mine using artificial neural network

2015 ◽  
Vol 1 (3-4) ◽  
pp. 69-77 ◽  
Author(s):  
Rahul ◽  
Manoj Khandelwal ◽  
Rajesh Rai ◽  
B. K. Shrivastva
2012 ◽  
Vol 430-432 ◽  
pp. 1700-1703
Author(s):  
Yan Kai Wu ◽  
Xian Song Sang ◽  
Bin Niu

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.


2013 ◽  
Vol 448-453 ◽  
pp. 3799-3802
Author(s):  
Feng Shan Han ◽  
Xin Li Wu

The artificial neural network has been widely used in various field of science and engineering. The artificial neural network has marvelous ability to gain knowledge. In this paper, according to principle of artificial neural network , Model of artificial neural network of rock bolt support of roadway of coal mine has been constructed,Learning system of BP artificial neural network has been trained,it is shown by engineering application that artificial neural network can handle imperfect or incomplete data and it can capture nonlinear and complex relationships among variables of a system. the artificial neural network is emerging as a powerful tool for modeling with the complex system. Method and parameters of rock bolt support of roadway of coal mine can be predicated accurately using artificial neural network, that is of significance and valuable to those subjects of investigation and design of mining engineering


Author(s):  
Bin Zeng ◽  
Wei Xiang ◽  
Joachim Rohn ◽  
Dominik Ehret ◽  
Xiaoxi Chen

Abstract. Landslides are one of the most common and damaging natural hazards in mountainous areas. However, due to the complex mechanisms that influence the activation of landslides, it can often be very difficult to predict exactly when a landslide will occur. Therefore, research on landslide prevention and mitigation mainly focuses on the distribution forecasting of unstable slopes that are prone to landslides in specific regions and under multiple external forces. The prediction of the spatial distribution of these unstable slopes, termed Landslide Susceptibility Zonation, is important in helping with government land-use planning and in reducing unnecessary loss of life and property. Researching unstable slopes in the Silurian stratum in Enshi region, China, this investigation established a GIS and artificial neural network (ANN)-based method to predict the distribution of potential landslides in this area. Based on the failure mechanism analysis of typical landslides in Silurian stratum, development of evaluation index system which represents the most relevant factors that influence the slope stability, and establishment of intelligent slope stability susceptibility prediction model by artificial neural network, the spatial distribution of unstable slope zones that are prone to landslides were predicted in the study area. The results were further well supported from remote sensing data and field investigations. This research proves that the spatial unstable slope prediction method based on intelligence theory and GIS technology is accurate and reliable.


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