文献类型：期刊文献

英文题名：Strength Model of Cemented Filling Body Based on a Neural Network Algorithm

作者：Deng, Daiqiang Liang, Yihua Cao, Guodong Fan, Jinkuan

第一作者：Deng, Daiqiang;邓代强

通信作者：Cao, GD[1];Liang, YH[2]

机构：[1]Xiangtan Univ, Coll Civil Engn & Mech, Xiangtan 411105, Peoples R China;[2]Guizhou Inst Technol, Coll Min Engn, Guiyang 550003, Guizhou, Peoples R China;[3]Guizhou Inst Technol, Ind Dev Res Ctr Guizhou, Guiyang 550003, Guizhou, Peoples R China

第一机构：Xiangtan Univ, Coll Civil Engn & Mech, Xiangtan 411105, Peoples R China

通信机构：corresponding author), Xiangtan Univ, Coll Civil Engn & Mech, Xiangtan 411105, Peoples R China;corresponding author), Guizhou Inst Technol, Ind Dev Res Ctr Guizhou, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

年份：2022

卷号：2022

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20222712305107);Scopus(收录号：2-s2.0-85133035357);WOS:【SCI-EXPANDED(收录号:WOS:000817678700001)】；

基金：This work was supported by the NSFC projects of China (51764009), the Guizhou Province Science and Technology Support Plan Project (Grant no. [2018]2836), the Provincial Natural Science Foundation of Hunan (2020JJ5538), the Scientific Research Fund of Hunan Province Education Department (20A475, 19C1736), and High-level Talent Gathering Project in Hunan Province (2019RS1059). The authors are grateful for the financial support for this research.

语种：英文

外文关键词：Cements - Strength of materials - Waste treatment

摘要：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.