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采选技术与矿山管理

基于正态隶属度—属性区间识别模型的岩爆倾向等级预测

  • 贡力 , 1, 2 ,
  • 陆丽丽 , 1 ,
  • 靳春玲 1 ,
  • 梁栋 3 ,
  • 周汉国 3 ,
  • 谢平 3
展开
  • 1. 兰州交通大学土木工程学院,甘肃 兰州 730070
  • 2. 兰州交通大学调水工程及输水安全研究所,甘肃 兰州 730070
  • 3. 中国中铁科学研究院有限公司,四川 成都 610000
陆丽丽(1994-),女,甘肃白银人,硕士研究生,从事土木工程建造与管理方面的研究工作。

贡力(1977-),男,江苏丹阳人,教授,博士生导师,从事深埋隧道及输水工程方面的研究工作。

收稿日期: 2021-03-05

  修回日期: 2021-08-29

  网络出版日期: 2022-09-14

基金资助

国家自然科学基金项目“西北盐渍干寒地区多因素耦合作用下长距离明渠的劣化机理研究”(51969011)

甘肃省科技计划资助项目“ 黄河流域突发水污染应急调控关键技术与应用”(20JR10RA274)

Propensity Grade Prediction of Rockburst Based on Normal Membership-Attribute Interval Recognition Model

  • Li GONG , 1, 2 ,
  • Lili LU , 1 ,
  • Chunling JIN 1 ,
  • Dong LIANG 3 ,
  • Hanguo ZHOU 3 ,
  • Ping XIE 3
Expand
  • 1. College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • 2. Institute of Water Diversion Engineering and Water Security, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • 3. China Railway Academy Co. , Ltd. , Chengdu 610000, Sichuan, China

Received date: 2021-03-05

  Revised date: 2021-08-29

  Online published: 2022-09-14

本文亮点

岩爆是地下工程开挖面临的关键问题之一,为了准确预测深埋隧洞中岩爆烈度倾向等级,提出了正态隶属度—属性区间识别模型的岩爆预测方法。针对岩爆倾向等级属于典型的多属性有序分割类问题,构建了属性区间识别模型,并将岩爆倾向等级划分为4个等级进行预测。根据岩爆发生的成因和机理,选取应力系数、脆性系数、弹性应变指数和岩石完整性系数作为预测指标,考虑各指标之间、指标与标准等级之间的交互关系,采用正态隶属度函数和Jousselme距离计算评价指标权重。结合13个深埋隧洞工程对该预测模型进行准确性测试,并以双江口水电站SPD9厂房为例进行工程实例验证,该模型预测结果与实际相吻合,证明该模型用于具体工程实践中是可行且有效的,研究结果可为类似深埋隧洞岩爆倾向等级预测提供新的思路。

本文引用格式

贡力 , 陆丽丽 , 靳春玲 , 梁栋 , 周汉国 , 谢平 . 基于正态隶属度—属性区间识别模型的岩爆倾向等级预测[J]. 黄金科学技术, 2022 , 30(3) : 404 -413 . DOI: 10.11872/j.issn.1005-2518.2022.03.037

Highlights

Rockburst is one of the key problems in underground engineering excavation,in order to accurately predict the grade of rockburst propensity in deep-buried tunnels,a rockburst prediction method based on the normal membership degree-attribute interval recognition model was proposed.Due to the rockburst propensity is a typical multi-attribute orderly segmentation problem,the attribute interval recognition model was constructed to divide the rockburst propensity into four grades for prediction.The occurrence of rockburst is affected by engineering geology,and the geological parameter is usually an interval value.The attribute interval recognition model can be better applied to the problem that each grade index value is an interval value.According to the mechanism of rockburst,the stress coefficient,brittleness coefficient,elastic strain index and rock integrity coefficient were selected as predictive indicators,considering the interaction between the indicators and the standard grade,the normal membership function and Jousselme distance were used to calculate the weight of the index.The method had different weights for the measured values of different indexes under the same index system,which can avoid the drawback that the traditional method didn’t consider the interaction relationship between the indexes,and improve the robustness of the model.The accuracy of the prediction model was tested with 13 deep-buried tunnel projects.Since the value of the averaging coefficient had a greater impact on the prediction accuracy of the model,in order to obtain the optimal value of the averaging coefficient,the step size was 0.1 and selected within the interval [0.05,0.95].The analysis shows that when the averaging coefficient is 0.65,the prediction accuracy of the model is the highest,which is 92.31%.The left bank of SPD9 in the Huangjiangkou hydropower project was used to verify the model.The prediction results are consistent with the actual rockburst propensity grade.The prediction results prove that the model is feasible and effective in specific engineering practice,and it can provide new ideas for predicting the rockburst propensity grade in similar deep-buried tunnels.

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科学家用石墨烯从电子垃圾提取金

据Mining.com网站报道,来自曼彻斯特大学、清华大学和中国科学院的研究人员证实石墨烯是“魔法石”,可以从金含量非常低(千万分之一)的废物特别是电子垃圾中提取这种贵金属。

在发表于《自然·通讯》(Nature Communications)期刊的论文中,科学家解释称,他们提出的方法是将石墨烯加进含有痕量金的溶液中,几分钟后,纯金就出现在石墨烯层中,而不需要其他化学品或能源驱动。此过程完成后,就可以通过简单地燃烧石墨烯来提取这种黄色金属。

他们的实验表明,1 g石墨烯足以提取近2 g黄金。由于石墨烯的成本低于每克0.10美元,而黄金价格为70美元/克,因此这种方法经济效益可观。

该研究通讯作者苏阳称,此方法本质上是一个简单的电化学过程,石墨烯和金离子之间独特的相互作用促进了这一过程,并产生了精准的选择性,只吸附金而不是其他离子或盐。

基于石墨烯的工艺提取能力强,选择性精准,几乎可以全部回收电子垃圾中的金。这为解决金可持续利用和电子垃圾面临的挑战提供了一个极具前景的解决方案。

“从文中看,石墨烯将电子垃圾变成了黄金”,该文通讯作者、首次成功分离石墨烯的诺贝尔奖获得者安德烈·盖姆(Andre Geim)认为。

“我们的发现不仅有可能实现此行业的持续发展,更重要的是凸显了原子级超薄材料与其原料即大宗材料的不同之处”。

脚注

中国矿业报

http://www.goldsci.ac.cn/article/2022/1005-2518/1005-2518-2022-30-3-404.shtml

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