Self-memorization model of dynamic system for predicting nonlinear displacement of slopes
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摘要: 边坡位移的准确预测对于边坡稳定性评价、边坡安全状态的预警以及滑坡灾害的控制具有重要意义。将“动力系统自记忆原理”引入到边坡位移时间序列预测研究。首先将量测得到的边坡位移时序数据视为描写边坡位移非线性动力学模型的一个特解，采用双向差分原理反导出边坡位移非线性常微分方程。以此作为微分动力核，运用自记忆原理建立了边坡位移预测的自记忆模型。将该方法用于三峡永久船闸边坡和卧龙寺边坡变形预测，研究结果表明：自记忆模型对于边坡位移预测具有较高的预测精度和较强的预测多个时序步位移的能力，从而为边坡位移预测提供了一条新途径。Abstract: It is of great engineering significance to accurately predict the displacement of slopes for the slope stability evaluation, slope failure forecast and catastrophe control of landslides. The self-memorization principle of dynamic system is introduced into the slope displacement prediction. By treating the time series data of monitored slope displacement as the particular solution of the nonlinear dynamic model of slopes, the nonlinear ordinary differential equation of slope deformation is deduced based on the bilateral difference principle. By using the deduced nonlinear differential equation of slope displacement as a differentiation dynamic kernel, the self-memorization model for slope displacement prediction is established based on the self-memorization theory. The model is applied to modeling and predicting the deformation time series data monitored at the slope of permanent ship lock for the Three Gorges Project and the Wolongsi slope. The case studies show that the self-memorization model is valid and feasible in predicting the displacement of slopes with good modeling and prediction accuracy and better ability of predicting displacement in more time intervals, thus a new approach for predicting deformation of slopes is proposed.
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