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

作　　者：姚亚锋[1,2] 程桦[1,3] 荣传新[4] 姚直书[4] 薛维培 YAO Yafeng;CHENG Hua;RONG Chuanxin;YAO Zhishu;XUE Weipei(Post-doctoral Research Station of Safety Science and Engineering,Anhui University of Science and Technology,Huainan 232001,China;School of Architectural Engineering,Nantong Vocational College,Nantong 226001,China;School of Resources and Environmental Engineering,Anhui University,Hefei 230022,China;School of Civil Engineering and Architecture,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学安全科学与工程博士后科研流动站,安徽淮南232001 [2]南通职业大学建筑工程学院,江苏南通226001 [3]安徽大学资源与环境工程学院,安徽合肥230022 [4]安徽理工大学土木建筑学院,安徽淮南232001

出　　处：《煤炭学报》2020年第3期1089-1098,共10页Journal of China Coal Society

基　　金：国家自然科学基金资助项目(51874005,51474004);南通市级科技发展计划资助项目(MS12018054)。

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。In order to resist complex and changeable loading of underground structure engineering effectively,and improve 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 factors 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 model 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 expectation 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 algorithm,the integrated fuzzy random distribution of structural materials,and the geometric parameters and calculation model under big data environment,an ultimate bearing capacity fuz

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 

分 类 号：TD352[矿业工程—矿井建设]