【作者】 王伟；

【导师】 谢学斌；

【作者基本信息】 中南大学 ， 地下空间科学与工程， 2011， 硕士

【摘要】 基坑工程是一个复杂的岩土工程问题,基坑的变形受到众多复杂因素的综合影响,而传统基坑工程设计方法在变形控制方面很难达到预期的效果。通过现场施工监测的方法来及时掌握基坑支护结构及周围环境的变形情况,对监测数据进行分析处理,预测基坑的变形发展趋势并及时优化设计与指导施工是目前防止工程事故发生的主要方法,因此提高基坑位移预测的精度和稳定性,研究基坑位移预测新方法具有重大的工程意义。基坑位移系统具有其确定性,也具有其随机性,而传统的基坑位移时间序列分析方法一般都是以随机论为基础,是一种不确定性方法。本文试图将一种随机与动力相结合的非线性方法——动力系统自忆性原理引入到基坑位移预测中来,以寻求一条基坑位移预测的新途径。(1)研究了利用传统双向差分法反演基坑位移时间序列的动力微分方程,并在此基础上建立双向差分自记忆模型的方法。(2)利用灰色系统理论反演基坑位移动力系统的灰色微分方程,并以此作为动力微分方程建立灰色自记忆模型。(3)在已有研究基础上提出通过曲线拟合的方法求得系统动力微分方程,建立趋势曲线自记忆预测模型的方法。将三种自记忆模型应用于深圳市地铁购物公园站基坑支护结构水平位移预测,结果表明三种自记忆模型均具有较好的拟合与预测精度,但双向差分自记忆模型推导过程复杂、计算繁琐,建模试算工作量大,而灰色自记忆模型和趋势曲线自记忆模型比双向差分自记忆模型具有更好可操作性,且趋势曲线自忆性模型在时间序列出现剧烈波动点时拟合与预测结果更好,是一种更加简单实用的自记忆预测模型。本文将自记忆原理引入到基坑位移时间序列分析中来,为基坑位移预测提供了新方法、新理论。更多还原

【Abstract】 In the geotechnical engineering, foundation pit engineering is a complex existence for whose deformation is integrated influenced by many factors. The traditional designing methods for controlling deformation are difficult to achieve the desired effects. So method that to master the deformation condition of supporting structure and surrounding environment through site construction monitoring and then to analysis and process the monitoring data for predicting the deformation trend and promptly optimal designing and guiding the construction is the main method to avoid accidents happened. To improve the accuracy and stability of foundation displacement prediction have great significance for researching the new method for predicting foundation displacement.Pit displacement system has its certainty and randomness. While the traditional time series analysis method of foundation displacement is commonly based on stochastic theory which belongs to an uncertainty method. This paper tries to introduce a nonlinear method namely dynamic system self-memorization principle which is combined with random and dynamic into the pit displacement prediction, and to seek a new way for predicting displacement. The paper is down below:Firstly, studying the method that using traditional two-way finite difference method to inverse dynamic differential equation of displacement time sequence and then building self-memory model of two-way difference on this basis.Secondly, utilizing the grey system theory to inverse gray differential equation of displacement dynamics system and taking it as the dynamic differential equation to build grey self-memory modelThirdly, proposing a method that to seek system dynamic differential equation through the curve fitting method based on the existing research and then establishing trend curve-self-memory forecasting model.We apply three self-memory models in predicting the horizontal displacement prediction of Foundation Pit supporting structure in Shenzhen metro shopping park station. The results show that all three self-memory models have good fitting and predictive accuracies. But the derivation processes of two-way difference self-memory model are complicated, tedious calculation and heavy workload for modeling. However, grey self-memory model and trend curve self-memory model have better maneuverability than two-way difference self-memory model. Moreover, the trend curve self-memorial model has better fitting and forecasting results when volatility points are happened to time series. So it is a more simple and practical self-memory forecasting model.This article introduces self-memorization principle into the time series analysis of foundation pit displacement and for which it provides a new theory and new method in foundation pit displacement prediction.更多还原