Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression
In view of the fact that the face-end roof fall under repeated mining of close-distance coal seams seriously affects the normal production of the working face, this paper takes working face 17101 as the background, different influencing factors of face-end roof caving exposed to repeated mining are...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5755055 |
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| author | Qiang Li Dezhong Kong Guiyi Wu Zhijie Wen Yuqi Shang |
| author_facet | Qiang Li Dezhong Kong Guiyi Wu Zhijie Wen Yuqi Shang |
| author_sort | Qiang Li |
| collection | DOAJ |
| description | In view of the fact that the face-end roof fall under repeated mining of close-distance coal seams seriously affects the normal production of the working face, this paper takes working face 17101 as the background, different influencing factors of face-end roof caving exposed to repeated mining are analyzed through field observation of mine pressure data, different calculation schemes are obtained by using the orthogonal experiment, the subsidence of the face-end roof is taken as the judgment index, UDEC simulation software is used to calculate the subsidence of the face-end roof when different influencing factors change, and the application of SPSS statistical software is used for various parameters of multivariate linear regression analysis. The research results show that the influence degree of different factors from large to small is, respectively, mining height > tip-to-face distance > advancing speed > distance of coal seams > surrounding rock strength > support setting load. It is necessary to strengthen the coordination of all influencing factors and comprehensively control the stability of the face-end roof exposed to repeated mining. Through the analysis of the regression model, it is found that there is no collinearity among the influencing factors, which has a significant influence on the regression equation and regression coefficient, and the multiple linear regression equation has a good fitting effect. The model can predict the stability of the face-end roof exposed to repeated mining, which provides a basis for controlling the face-end roof exposed to repeated mining. |
| format | Article |
| id | doaj-art-9d8430296965484e913d85ad811f0f60 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-9d8430296965484e913d85ad811f0f602025-02-03T01:24:46ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/57550555755055Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear RegressionQiang Li0Dezhong Kong1Guiyi Wu2Zhijie Wen3Yuqi Shang4College of Mining, Guizhou University, Guiyang 550025, Guizhou, ChinaCollege of Mining, Guizhou University, Guiyang 550025, Guizhou, ChinaCollege of Mining, Guizhou University, Guiyang 550025, Guizhou, ChinaKey Laboratory of Mining Disaster Prevention and Control, Qingdao 266590, ChinaCollege of Mining, Guizhou University, Guiyang 550025, Guizhou, ChinaIn view of the fact that the face-end roof fall under repeated mining of close-distance coal seams seriously affects the normal production of the working face, this paper takes working face 17101 as the background, different influencing factors of face-end roof caving exposed to repeated mining are analyzed through field observation of mine pressure data, different calculation schemes are obtained by using the orthogonal experiment, the subsidence of the face-end roof is taken as the judgment index, UDEC simulation software is used to calculate the subsidence of the face-end roof when different influencing factors change, and the application of SPSS statistical software is used for various parameters of multivariate linear regression analysis. The research results show that the influence degree of different factors from large to small is, respectively, mining height > tip-to-face distance > advancing speed > distance of coal seams > surrounding rock strength > support setting load. It is necessary to strengthen the coordination of all influencing factors and comprehensively control the stability of the face-end roof exposed to repeated mining. Through the analysis of the regression model, it is found that there is no collinearity among the influencing factors, which has a significant influence on the regression equation and regression coefficient, and the multiple linear regression equation has a good fitting effect. The model can predict the stability of the face-end roof exposed to repeated mining, which provides a basis for controlling the face-end roof exposed to repeated mining.http://dx.doi.org/10.1155/2021/5755055 |
| spellingShingle | Qiang Li Dezhong Kong Guiyi Wu Zhijie Wen Yuqi Shang Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression Advances in Materials Science and Engineering |
| title | Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression |
| title_full | Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression |
| title_fullStr | Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression |
| title_full_unstemmed | Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression |
| title_short | Influence Factors’ Analysis of the Face-End Roof Leaks Exposed to Repeated Mining Based on Multiple Linear Regression |
| title_sort | influence factors analysis of the face end roof leaks exposed to repeated mining based on multiple linear regression |
| url | http://dx.doi.org/10.1155/2021/5755055 |
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