基于多变量混沌时间序列的煤矿斜井TBM施工动态风险预测  

Risk analysis on long inclined-shaft construction in coalmine by TBM techniques based on multiple variables chaotic time series

作　　者：侯公羽[1] 梁荣[1] 孙磊[1] 刘琳[1] 龚砚芬 

机构地区：[1]中国矿业大学(北京)力学与建筑工程学院,北京100083

出　　处：《物理学报》2014年第9期103-110,共8页Acta Physica Sinica

基　　金：国家自然科学基金委员会与神华集团有限责任公司联合资助重点项目(批准号:U1261212);中央高校基本科研业务费(批准号:2012KL02)资助的课题~~

摘　　要：在全面分析煤矿长斜井TBM(盾构)施工动态风险特点的基础上,利用多变量混沌时间序列预测方法对其进行预测.利用主成分分析法,确定影响煤矿长斜井TBM施工风险的主要成分.对煤矿长斜井TBM施工风险多变量时间序列进行相空间的重构,确定时间延迟τi和嵌入维数mi,采用小数据量法计算煤矿长斜井TBM施工多变量风险时间序列的最大Lyapunov指数,证明了其具有混沌特性,提出了一阶局域法与双隐层神经网络的组合预测模型,该模型能够对多变量风险时间序列随时间的变化进行预测.仿真实验表明,该预测模型误差小于单变量时间序列的预测误差,具有较强的预测能力和较好的预测效果,可为煤矿长斜井TBM施工风险分析与评估提供一种新的途径.Multi-variable chaotic time series are used to predict the long inclined-shaft construction in coalmine construction by TBM techniques, and principal component analysis （PCA） is used to determine the main factors that impact risk （shield） of the long inclined-shaft construction in coalmine by TBM techniques. Phase space of risk time series for construction by TBM are reconstructed; time delay and embedding dimension are determined. Maximum Lyapunov indexes of risk are obtained by using small data quantity method;it is found that the time series have characteristics of chaos. Prediction model is established using the combination of first-order local method and double hidden layer neural network. Simulation experiments show that the combined model has a strong ability of prediction and achieves better effect. As a result, it provides a new way for long inclined-shaft construction in coalmine by TBM techniques.

关 键 词：煤矿长斜井 TBM施工 风险预测 多变量时间序列 

分 类 号：TD262.12[矿业工程—矿井建设] O415.5[理学—理论物理]