Strength Model of Cemented Filling Body Based on a Neural Network Algorithm
As one of the key measures for comprehensive management of goaf in various mines, filling mining has been recognized by practitioners in recent years due to its functions (e.g., resource utilization of solid waste and thorough goaf treatment). The performance of the filling material is the core chal...
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| Veröffentlicht in: | Mathematical problems in engineering 2022-04, Vol.2022, p.1-10 |
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| creator | Deng, Daiqiang Liang, Yihua Cao, Guodong Fan, Jinkuan |
| description | As one of the key measures for comprehensive management of goaf in various mines, filling mining has been recognized by practitioners in recent years due to its functions (e.g., resource utilization of solid waste and thorough goaf treatment). The performance of the filling material is the core challenge of filling mining, and it is influenced by the settling speed, conveying characteristics, and filling body strength. To understand the strength characteristics of a cemented filling body composed of medium-fine tailings, in this study, filling material ratio tests under different content of cement, tailings, and water were conducted. A backpropagation (BP) neural network topology structure was established in this study. The strength after different curing times was used as the output variable to analyze the impact of the cement, tailings, and water content on the filling body. A 3-Hn-3 structural model was employed. When the number of hidden layers Hn was 7, the model achieved the best learning and training effect. The results show that the predicted value, which is close to the measured value (fitting accuracy of 92.43–99.92%; average error of 0.0792–7.5682%), satisfies the engineering requirements. The neural network model can be employed to predict the filling body’s strength and provide a good reference to analyze the change law in the filling body’s strength. |
| doi_str_mv | 10.1155/2022/2566960 |
| format | Article |
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The performance of the filling material is the core challenge of filling mining, and it is influenced by the settling speed, conveying characteristics, and filling body strength. To understand the strength characteristics of a cemented filling body composed of medium-fine tailings, in this study, filling material ratio tests under different content of cement, tailings, and water were conducted. A backpropagation (BP) neural network topology structure was established in this study. The strength after different curing times was used as the output variable to analyze the impact of the cement, tailings, and water content on the filling body. A 3-Hn-3 structural model was employed. When the number of hidden layers Hn was 7, the model achieved the best learning and training effect. The results show that the predicted value, which is close to the measured value (fitting accuracy of 92.43–99.92%; average error of 0.0792–7.5682%), satisfies the engineering requirements. The neural network model can be employed to predict the filling body’s strength and provide a good reference to analyze the change law in the filling body’s strength.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/2566960</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Aggregates ; Algorithms ; Back propagation networks ; Cement ; Coal mining ; Engineering ; Environmental protection ; Fillers ; Fractals ; Impact analysis ; Laboratories ; Mechanical properties ; Mines ; Moisture content ; Network topologies ; Neural networks ; Particle size ; Researchers ; Resource utilization ; Solid wastes ; Structural models ; Tailings</subject><ispartof>Mathematical problems in engineering, 2022-04, Vol.2022, p.1-10</ispartof><rights>Copyright © 2022 Daiqiang Deng et al.</rights><rights>Copyright © 2022 Daiqiang Deng et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c224t-18dde0dd8cde8b88d14006ce0551f0677b8ce7bb9b1ada6290f31af7078c2313</cites><orcidid>0000-0002-4988-1406 ; 0000-0002-9540-1231 ; 0000-0002-5339-1362 ; 0000-0002-7447-4538</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Yang, Mijia</contributor><contributor>Mijia Yang</contributor><creatorcontrib>Deng, Daiqiang</creatorcontrib><creatorcontrib>Liang, Yihua</creatorcontrib><creatorcontrib>Cao, Guodong</creatorcontrib><creatorcontrib>Fan, Jinkuan</creatorcontrib><title>Strength Model of Cemented Filling Body Based on a Neural Network Algorithm</title><title>Mathematical problems in engineering</title><description>As one of the key measures for comprehensive management of goaf in various mines, filling mining has been recognized by practitioners in recent years due to its functions (e.g., resource utilization of solid waste and thorough goaf treatment). The performance of the filling material is the core challenge of filling mining, and it is influenced by the settling speed, conveying characteristics, and filling body strength. To understand the strength characteristics of a cemented filling body composed of medium-fine tailings, in this study, filling material ratio tests under different content of cement, tailings, and water were conducted. A backpropagation (BP) neural network topology structure was established in this study. The strength after different curing times was used as the output variable to analyze the impact of the cement, tailings, and water content on the filling body. A 3-Hn-3 structural model was employed. When the number of hidden layers Hn was 7, the model achieved the best learning and training effect. The results show that the predicted value, which is close to the measured value (fitting accuracy of 92.43–99.92%; average error of 0.0792–7.5682%), satisfies the engineering requirements. 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The performance of the filling material is the core challenge of filling mining, and it is influenced by the settling speed, conveying characteristics, and filling body strength. To understand the strength characteristics of a cemented filling body composed of medium-fine tailings, in this study, filling material ratio tests under different content of cement, tailings, and water were conducted. A backpropagation (BP) neural network topology structure was established in this study. The strength after different curing times was used as the output variable to analyze the impact of the cement, tailings, and water content on the filling body. A 3-Hn-3 structural model was employed. When the number of hidden layers Hn was 7, the model achieved the best learning and training effect. The results show that the predicted value, which is close to the measured value (fitting accuracy of 92.43–99.92%; average error of 0.0792–7.5682%), satisfies the engineering requirements. 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| subjects | Aggregates Algorithms Back propagation networks Cement Coal mining Engineering Environmental protection Fillers Fractals Impact analysis Laboratories Mechanical properties Mines Moisture content Network topologies Neural networks Particle size Researchers Resource utilization Solid wastes Structural models Tailings |
| title | Strength Model of Cemented Filling Body Based on a Neural Network Algorithm |
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