【摘要】 高边坡受爆破、地震等强外界作用时,位移监测值会出现明显跳跃。有效辨识测值突变位置,消除或削弱位移突变对测值序列整体数值特征的影响,是提高高边坡位移监控模型拟合和预测精度的关键问题之一。基于高边坡系统演化过程中的非线性动力学特性,应用相空间重构、云模型等数值分析手段,研究了高边坡位移突变辨识等的实现方法;在对高边坡位移与影响因素相关分析的基础上,探讨了考虑动力学结构突变影响的位移预测模型构建原理与算法。该模型重点依据最近一次位移突变后的监测资料,考虑了突变后形成的相对稳定的高边坡动力系统特性,因而可以有效提高监控模型的拟合和预测精度。更多还原

【Abstract】 The displacement monitoring values of the high slopes exhibit notable mutations when the slope is affected by blasting,earthquakes,or other strong external effects.Identification of the mutation positions of the measured values and elimination of the effects of the displacement mutation on the numerical characteristics of the monitoring sequences are one of the key problems on the improvement of the fitting and prediction precision of the displacement monitoring model of high slopes.Due to the nonlinear dynamic behavior in the evolution process of the high slope system,the phase space reconstruction and the cloud model were used to study the implementation method for the identification of the displacement mutation of high slopes..Furthermore,the building principle and algorithm of displacement prediction model considering the effects of dynamic structure mutation were discussed based on the correlation analysis between the displacement of high slopes and the affecting factors.The proposed model was dependent on the up-to-date monitoring data after the last displacement mutation,and considered the relatively stable dynamic system characteristics of the high slopes formed after the mutations,which can improve the fitting and prediction precision of the displacement monitoring model of high slopes effectively.更多还原