滑坡非线性演化行为的自组织进化识别

EVOLUTIONARY SELF-ORGANIZING IDENTIFICATION OF NONLINEAR DYNAMICS OF LANDSLIDES

滑坡行为表现出复杂的非线性演化特征,位移是滑坡演化过程中所反馈出的重要信息之一。引入进化算法的全局优化思想,结合时间序列分析基本理论,以斜坡位移时间序列为基础,将遗传规划和遗传算法有机结合在一起,设计了一种模型结构和参数分别进化、共同识别的进化方案,实现对斜坡演化的非线性动力学模型结构和参数的全局最优识别。以新滩及八尺门滑坡为例对滑坡的发展孕育过程进行分析,结果表明,新方法识别获得的非线性动力学模型预测效果较理想,而且表现出较高的自组织进化识别能力。

Landslides are characterized with complex nonlinear-dynamic behavior involving many uncertain factors. The physical-based modeling approach is often very difficult to fulfill. As an alternative, based on the time series analysis theory and the idea that the displacement is one of the most important information reflecting the sliding state during the evolution of landslides, a new hybrid evolutionary method, combining genetic algorithm and genetic programming, was proposed to identify the evolution character of landslides from the observed displacement time series. In this method, the model structure and model parameters are evolved by using the symbol regression techniques of genetic programming and genetic algorithm, respectively, and a global optimal nonlinear dynamic input-output model for predicting the state of landslides is fulfilled through data analysis. Models of input and output are the displacements history and future displacements, respectively. Applications to the evolution analysis of the Xintan landslide and Bachimen landslide were performed and the results proved the efficiency of the new method. Furthermore, the new algorithm shows significant power of self-organization.