• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于大数据挖掘的深土井壁极限承载力模糊随机模型
  • Title

    Fuzzy random analysis on ultimate bearing capacity based on big data mining in deep alluvium

  • 作者

    姚亚锋程桦荣传新姚直书薛维培

  • Author

    YAO Yafeng1,2 ,CHENG Hua1,3 ,RONG Chuanxin4 ,YAO Zhishu4 ,XUE Weipei4

  • 单位

    安徽理工大学 安全科学与工程博士后科研流动站南通职业大学 建筑工程学院安徽大学 资源与环境工程学院安徽理工大学 土木建筑学院

  • Organization
    1. Post-doctoral Research Station of Safety Science and Engineering,Anhui University of Science and Technology,Huainan  232001,China; 2. School of Ar-chitectural Engineering,Nantong Vocational College,Nantong  226001,China; 3. School of Resources and Environmental Engineering,Anhui University,Hefei  230022,China; 4. School of Civil Engineering and Architecture,Anhui University of Science and Technology,Huainan  232001,China
  • 摘要

    为有效抵御地下结构工程中复杂多变的外荷载,提升深土井筒支护的安全可靠性,运用两淮矿区深厚冲积层井壁为原型,按相似性原理浇筑钢筋混凝土井壁模型,进行了大量钢筋混凝土井壁模型的极限承载力试验,结果发现影响井壁极限承载力的主要因素有混凝土抗压强度、厚径比和配筋率。其中,混凝土抗压强度对井壁承载力影响较为明显,配筋率影响较弱,但各影响因素在深厚冲积层实际工程中又伴随着不同程度的不确定性。针对深厚冲积层井筒施工过程中极限承载力及其影响因素的模糊随机性,以大量井壁试验和两淮矿区的钢筋混凝土井筒工程参数作为大数据样本集,分析结构材料、几何参数和计算模式的不确定分布情况,得到混凝土抗压强度、厚径比和配筋率的模糊随机分布规律。采用最大期望算法(EM)优化传统的大数据HMM挖掘模型,分别经过E步骤计算极大似然估计值和M步骤计算参数期望估计,改进后模型经过两次模糊随机过程,相比原算法具有误差小、效率高和收敛快等优点,更能满足实际地下工程中的不确定特性。基于改进后的大数据挖掘HMM算法,综合大数据环境下的材料性能、几何参数和计算模式的模糊随机分布,建立大数据挖掘井壁极限承载力模糊随机模型,实例证明该模型更加可靠合理,更具有工程实用价值。


  • Abstract
    In order to resist complex and changeable loading of underground structure engineering effectively,and im- prove the safety and reliability of the shaft lining,regarding shafts in the deep alluvium of Huainan and Huaibei mining area as the prototype and pouring reinforced concrete shaft lining model according to the similarity principle,a lot of ultimate bearing capacity tests of reinforced concrete lining models are conducted. The result shows that the main fac- tors affecting load bearing capacity are concrete compression strength,ratio of lining thickness to inner radius and reinforcement ratio. Among them,the impact of concrete compressive strength on shaft lining bearing capacity is obvious, and the impact of reinforcement ratio is weak. However,various influencing factors are accompanied by varying degrees of uncertainty in practical engineering. Aiming at the fuzzy random of ultimate bearing capacity in deep alluvium,based on the sample big data set of shaft lining structure parameters and tests of high strength reinforced concrete in Huainan and Huaibei mining area,the uncertainty distribution of structural materials,geometric parameters and calculation mod- el are analyzed to obtain the fuzzy random distributive rules of concrete compression strength,ratio of lining thickness to inner radius and reinforcement ratio. The traditional data mining HMM model is improved by using the algorithm of maximum expected (EM). The maximum likelihood estimate value is calculated in step E and the parameter expecta- tion estimate is calculated in step M respectively. The improved model has gone through two fuzzy random processes. Compared with the original algorithm,it has the advantages of small error,high efficiency and fast convergence,thus can better suit the uncertain characteristics of actual underground engineering. Based on the improved data mining al- gorithm,the integrated fuzzy random distribution of structural materials,and the geometric parameters and calculation model under big data environment, an ultimate bearing capacity fuzzy random model with big data mining of high strength reinforced concrete shaft lining has been set up,and proved to be more reasonable and practical for engineer- ing,thus providing reliable references for the design of reinforced concrete shaft lining structural parameters in deep al- luvium in the future.
  • 关键词

    钢筋混凝土井壁极限承载力模糊随机结构参数大数据挖掘HMM模型

  • KeyWords

    reinforced concrete shaft lining;ultimate bearing capacity;fuzzy random;structural parameters;data min- ing;HMM model

  • 基金项目(Foundation)
    国家自然科学基金资助项目(51874005,51374010,51474004);江苏省建设系统科技计划资助项目(2017ZD062);南通市级科技发展计划资助项目(MS12018054)
  • DOI
  • Citation
    YAO Yafeng,CHENG Hua,RONG Chuanxin,et al. Fuzzy random analysis on ultimate bearing capacity based on big data mining in deep alluvium[ J]. Journal of China Coal Society,2020,45 (3):1089 -1098.
  • 相关文章

主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会

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