Fuzzy rule based classification method of surrounding rock stability of coal roadway using artificial intelligence algorithm

2021 ◽  
pp. 1-9
Author(s):  
Guangzhe Deng ◽  
Yingkai Fu
Keyword(s):  
Artificial Intelligence ◽  
Classification System ◽  
Local Minimum ◽  
Fuzzy Rule ◽  
Surrounding Rock ◽  
Coal Roadway ◽  
Artificial Intelligence Algorithm ◽  
Stability Classification ◽  
The Stability

As the stability of surrounding rock of coal roadway is affected by many factors, which makes the classification result hard to be consistent with the field practice. To solve the above problems, this paper proposes a method for the classification of stability of rock which is present in roadway of coal using the artificial intelligence algorithm. In this paper, the influencing factors of stability of rock which is present in roadway are analyzed, and seven influential factors are selected as classification indexes. To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively. Based on the learning model available, classification system based on fuzzy rule have been implemented and yielded better behavior in the situation of uncertain data sets. Finally, the stability classification model of surrounding rocks of coal roadway using BP-ANN was established in MATLAB environment, and the model was applied to 13 data samples of coal roadway for testing, with the identification rate of 92.3%. The experimental results verify that the method proposed based on fuzzy rule classification system in this paper has a high accuracy of type identification and is applicable to the stability classification of surrounding rock in the coal roadway.

scholarly journals Surrounding Rock Stability Classification Method of Coal Roadway Based on In Situ Stress

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongjie Yang ◽  
Gang Huang ◽  
Lingren Meng
Keyword(s):  
Fuzzy Clustering ◽  
In Situ Stress ◽  
Surrounding Rock ◽  
Evaluation Indexes ◽  
Rock Stability ◽  
Coal Roadway ◽  
Fuzzy Clustering Method ◽  
Stability Classification

In situ stress is one of the most important factors affecting surrounding rock stability classification of coal roadway. Most surrounding rock stability classification methods do not fully consider the influence of in situ stress. In this paper, the author applied a fuzzy clustering method to the classification of surrounding rock stability of coal roadway. Taking into account the complexity of the classification of surrounding rock, some factors such as the strength of surrounding rock, in situ stress, the main roof first weighting interval, the size of the chain pillar, and the immediate roof backfilled ratio are selected as the evaluation indexes. The weight coefficients of these evaluation indexes are determined by unary regression and multiple regression methods. Using fuzzy clustering and empirical evaluation method, the classification model of surrounding rock stability of coal roadway is proposed, which is applied to 37 coal roadways of Zibo Mining Group Ltd., China. The result is in good agreement with practical situation of surrounding rock, which proves that the fuzzy clustering method used to classify the surrounding rock in coal roadway is reasonable and effective. The present model has important guiding significance for reasonably determining the stability category of surrounding rock and supporting design of coal roadway.

Classification of Surrounding Rock of Bolt Supported in Coal Mine Roadway

2014 ◽  
Vol 986-987 ◽  
pp. 775-778 ◽  
Author(s):  
Feng Shan Han ◽  
Li Song
Keyword(s):  
Coal Mine ◽  
Classification System ◽  
Knowledge Engineering ◽  
Surrounding Rock ◽  
Rock Classification ◽  
Coal Roadway ◽  
Mine Roadway ◽  
Important Significance ◽  
Bolt Support

Classification of Surrounding rock of roadway of Coal mine bolt supporting is the basis of bolting design, To scientifically design coal roadway bolting, classification of surrounding rock of coal roadway bolting must first be carry out. in this paper, the comprehensive application of knowledge engineering, rock mechanics, we developed the surrounding rock classification system for the bolt support of coal mine roadway engineering, the surrounding rock classification system is practical easy to use, the bolt supporting of roadway surrounding rock classification results to the coal mine roadway bolt support has important significance and practical value.

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

scholarly journals Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248809
Author(s):  
Anna Lind ◽  
Ehsan Akbarian ◽  
Simon Olsson ◽  
Hans Nåsell ◽  
Olof Sköldenberg ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knee Joint ◽  
Classification System ◽  
Plain Radiograph ◽  
Treatment Strategies ◽  
University Hospital ◽  
Complication Rates ◽  
Radiographic Images ◽  
Femur Fractures

Background Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system. Methods We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002–2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session. Results We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance. Conclusion Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint.

Classification of Children With Specific Language Impairment

1999 ◽  
Vol 42 (5) ◽  
pp. 1195-1204 ◽  
Author(s):  
Gina Conti-Ramsden ◽  
Nicola Botting
Keyword(s):  
Language Impairment ◽  
Specific Language Impairment ◽  
Classification System ◽  
Specific Language ◽  
Large Project ◽  
Using Data ◽  
The Stability

This paper reports on the longitudinal results of a large project involving 242 seven-year-old children attending language units in England. Following our work outlining 6 subgroups of children with language impairment (Conti-Ramsden, Crutchley, & Botting, 1997), we examine the stability of the 6 subgroups of children with specific language impairment already identified, using data collected from the same children at age 8 years. The findings suggest there is considerable stability in the patterns of difficulties delineated by the classification system involving 6 subgroups. Poorer stability was evident in the classification of the children across time with 45% of children moving across subgroups. The membership stability of the proposed classification system was very similar to that found when the children were classified into 3 subgroups following another well-known system (Rapin, 1996). The findings are discussed with particular reference to issues surrounding the classification of children with SLI.

Stress Distribution Characteristics in Rock Surrounding Heading Face and its Relationship with Temporary Supporting

2014 ◽  
Vol 568-570 ◽  
pp. 1684-1689
Author(s):  
Zhong Han Chen
Keyword(s):  
Coal Mine ◽  
Surrounding Rock ◽  
Zenith Distance ◽  
Rock Stability ◽  
Coal Roadway ◽  
Temporary Support ◽  
The Stability ◽  
Roadway Deformation ◽  
Mine Roof ◽  
Roof Deformation

To solve the problem of underground tunneling face from the empty top, using FLAC3D analysis software, surrounding rock stability for coal roadway 2-1121 of Ganhe Coal Mine are analyzed in numerical calculation. (1) During the tunneling, distance drivage face head-on 0.5-1m at the roof of roadway deformation and destruction features are more obvious, the two sides of roadway are even more significant. (2) Ganhe Coal Mine roof deformation has been established with different empty the experience formula of the zenith distance, obtained Ganhe underground tunneling face reasonable empty zenith distance is 3.5m. (3) Temporary support can obviously reduce roof deformation, reduce thickness of plastic zone of the top, to improve the stability of surrounding rock tunneling faces.

Study and Application of Three-Step Supporting Design Method in Coal Roadway

2013 ◽  
Vol 353-356 ◽  
pp. 252-257
Author(s):  
Ren Liang Shan ◽  
Xiang Song Kong ◽  
Ji Jun Zhou ◽  
Wen Feng Zhao ◽  
Yu Tao ◽  
...  
Keyword(s):  
Design Method ◽  
Surrounding Rock ◽  
Second Step ◽  
Preliminary Design ◽  
The Third ◽  
Coal Roadway ◽  
Roadway Stability ◽  
Simulation Calculation ◽  
Roadway Support ◽  
The Stability

Scientific supporting design is of great significance to ensure coal roadway stability. The three-step supporting design method is put forward for coal roadway support: The first step is preliminary design, determine the range of each supporting parameter according to the theoretical calculation and supporting experiences; the second step is numerical simulation calculation, choose the reasonable one through the comparison of schemes; the third step is field monitoring, verify the scheme applicability. After applying the three-step supporting design method to study 3# coal seam roadway in Guandi mine, the optimal supporting scheme is obtained, and good results of underground roadway are achieved, which ensure the stability of roadway surrounding rock. Meanwhile, some rules are summarized which provide references for future roadway supporting design.

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