基于最大Lyapunov指数的高边坡安全监控优化模型

A Safety Monitoring Optimal Model for High Slope Based on Largest Lyapunov Exponent

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,组合应用相空间重构、最大Lyapunov指数、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.该模型重点依据的是最近一次位移突变后的监测资料,考虑的是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.

Displacement monitoring values exhibit notable mutations if a high slope is affected by blasting, earthquakes, or other strong outside effects. Among the key problems in improving the fitting and prediction precision of the high slope displacement monitoring model include effectively identifying the mutation posi-tions of measured values and eliminating or weakening the effect of mutations on the numerical characteristics of the overall series of measurements. This paper Studies the realization method of high slope displacement mutations identification based on the nonlinear dynamic behavior in the high slope system evolution process, which combines phase space reconstruction, the largest Lyapunov exponent, the cloud model, and other nu-merical analysis methods. Furthermore, based on the correlation analysis between high slope displacement and the affecting factors, the current work discusses the building principle and algorithm of displacement con- sidering dynamic structure mutation effects. The proposed model depends on up-to-date monitoring data after the last displacement mutations, and considers the relatively stable high slope dynamic system characteristics formed after these mutations. Therefore, the fitting and prediction precision of the high slope displacement monitoring model can be effectively improved.