姚亚锋, 程桦, 荣传新, 姚直书, 薛维培. 基于大数据挖掘的深土井壁极限承载力模糊随机模型[J]. 煤炭学报, 2020, 45(3). DOI: 10.13225/j.cnki.jccs.2019.0318
引用本文: 姚亚锋, 程桦, 荣传新, 姚直书, 薛维培. 基于大数据挖掘的深土井壁极限承载力模糊随机模型[J]. 煤炭学报, 2020, 45(3). DOI: 10.13225/j.cnki.jccs.2019.0318
YAO Yafeng, CHENG Hua, RONG Chuanxin, YAO Zhishu, XUE Weipei. Fuzzy random analysis on ultimate bearing capacity based on big data mining in deep alluvium[J]. Journal of China Coal Society, 2020, 45(3). DOI: 10.13225/j.cnki.jccs.2019.0318
Citation: YAO Yafeng, CHENG Hua, RONG Chuanxin, YAO Zhishu, XUE Weipei. Fuzzy random analysis on ultimate bearing capacity based on big data mining in deep alluvium[J]. Journal of China Coal Society, 2020, 45(3). DOI: 10.13225/j.cnki.jccs.2019.0318

基于大数据挖掘的深土井壁极限承载力模糊随机模型

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

  • 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.

     

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