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金属矿山 ›› 2016, Vol. 45 ›› Issue (03): 40-44.

• 采矿工程 • 上一篇    下一篇

似膏体充填材料配比的BP网络优化方法

顾清恒1,2   

  1. 1.山东科技大学矿业与安全工程学院,山东 青岛 266590;2.矿山灾害预防控制省部共建国家重点实验室培育基地,山东 青岛 266590
  • 出版日期:2016-03-15 发布日期:2016-05-17
  • 基金资助:

    * 国家自然科学基金项目(编号:51274133,51474137),矿山灾害预防控制省部共建国家重点实验室培育基地开放基金项目(编号:MDPC2013KF12)。

Mixing Proportion of Paste-like Filling Material based on Back-propagation Neural Network

Gu Qingheng1,2   

  1. 1.School of mining and safety engineering,Shandong University of Science and Technology,Qingdao 266590,China;2.State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology,Qingdao 266590,China
  • Online:2016-03-15 Published:2016-05-17

摘要: 为了探究似膏体充填材料各组分对材料性能的影响及最优配比,以实验室做的16组配比试验数据为样本,建立了材料组分等因素与分层度、塌落度及长期强度之间的5-10-3 BP网络模型,并利用该模型搜索的最优配比方案进行现场试验。结果表明:建立的网络模型最大相对预测误差为6.08%;胶结料与骨料共同决定充填体的强度;高比例煤矸石配以精确比例的胶结料和细骨料可形成强结构充填体;当粉煤灰掺量为水泥质量的3~4倍,河砂掺量约等于煤矸石质量时,材料的综合性能最好;利用优化后的材料配比方案进行巷旁充填,巷道围岩变形较小且较稳定。

关键词: BP神经网络, 似膏体, 料浆配比, 优化选择

Abstract: In order to explore the effect of material components on material properties and the optimal ratio of paste-like filling material,the BP network model was established based on the 16 groups results of ratio test.The structure of the model is the components and 5-10-3 type of layered degree,slump and long-term strength.By means of entering the refined ratio parameters into prediction model,the optimal ratio were obtained.The results showed that,the maximum relative error of the network model is 6.08%; The amount of cementing material and aggregate determine the long-term strength of materials together.High proportion of coal gangue is beneficial for forming strong structure filling body with the accurate ratio of other components.When the proportion of fly ash is 3 to 4 times that of cement and the proportion of sandstorm is almost equal to coal gangue,the slurry has the best overall performance.The gob-side entry retaining was backfilled according to the optimal material ratio in experimental mine,resulting that the convergence of surrounding rock was light and stable.

Key words: Back-propagation neural network, Paste-like material, Slurry ratio, Optimization