参数自适应扩展卡尔曼滤波理论及其在隧道变形预测中的应用

发布时间：2018-09-10 11:10

【摘要】：随着我国隧道工程快速全面的发展,单体规模更加庞大、技术规格要求更高、地质环境更加恶劣的隧道建设工程在不断增多,这些工程在采用新奥法进行设计与建设时,从施工控制、理论校验的角度出发,隧道现场监控量测就显得更加的意义重大。然而,由于监测手段以及后期数据处理分析方法的不完善、反馈的信息质量不高等问题,使得现场监控量测作业在校验设计及指导施工方面远未发挥其应有的作用。针对上述问题,本文作者提出了基于参数自适应扩展卡尔曼滤波理论的隧道变形预测的研究课题,并基于大量的现场监测数据和地质资料,在已有研究成果基础上进行了深入系统地研究。首先,对现有隧道施工监控量测数据分析方法进行了归纳总结,重点分析了回归分析法、时间序列分析法、灰色预测法以及卡尔曼滤波法的原理和计算方法,并比较了各方法在隧道变形预测分析中的优势与不足。其次,基于比较分析结果,确立了以卡尔曼滤波为主要方法的研究思路。考虑到隧道围岩的变形与发展,受较多难以定量化的不明因素影响,其变形与发展具有非线性的特点,而卡尔曼滤波是一种处理线性问题的有效方法,所以联想到了化非线性为线性方法,即扩展卡尔曼滤波法。由于该滤波方法需要已知的精确模型参数及噪声统计,而实际应用中往往难以满足上述前提,因此提出了自适应的方法。鉴于目前较多的自适应方法集中于以噪声统计为基础的方差自适应研究,而较少从模型偏差角度来解决参数自适应的方法,本文创造性地通过残差特性因子进行滤波的敛散性评判,基于敛散趋势进行相应的模型参数缩放调节,实现了模型参数自适应,防止滤波发散失效。最后,运用提出的参数自适应扩展卡尔曼滤波方法,建立了隧道拱顶下沉及周边位移的预测模型,并结合大量的现场量测数据,进行隧道洞内变形预测分析处理。将参数自适应扩展卡尔曼滤波和标准扩展卡尔曼滤波的预测结果与观测值进行对比,分析了数自适应扩展卡尔曼滤波对隧道洞内变形的预测性能与优势,探讨了其适用性。本文所做研究工作,立足于学科前沿,采用先进的数学方法和手段对隧道洞内变形预测进行了研究,具有较高的理论和应用价值,为隧道监控量测数据的处理提供了一种新的有效分析手段。
[Abstract]:With the rapid and comprehensive development of tunnel engineering in our country, the scale of individual is more huge, the technical specifications are higher, and the geological environment is worse, the number of tunnel construction projects is increasing. When these projects are designed and constructed by the new Austrian method, from the point of view of construction control and theoretical checking, the field monitoring and measurement of tunnel becomes more and more important. However, due to the imperfection of monitoring means and the method of data processing and analysis, and the low quality of feedback information, the field monitoring and measurement work is far from playing its due role in checking design and guiding construction. Based on a large number of field monitoring data and geological data, this paper makes a thorough and systematic study on the existing research results. Firstly, the existing data analysis methods of tunnel construction monitoring and measurement are summarized, with emphasis on regression analysis, time series analysis and gray. The principle and calculation method of color prediction method and Kalman filter method are compared, and the advantages and disadvantages of each method in tunnel deformation prediction and analysis are compared. Secondly, based on the comparative analysis results, the main research method of Kalman filter is established. Because of the primitive effect, its deformation and development are nonlinear, and Kalman filter is an effective method to deal with linear problems, extended Kalman filter (EKF) is proposed to transform nonlinear into linear method. In view of the fact that more adaptive methods focus on variance adaptation based on noise statistics and less on model bias to solve parameter adaptation, this paper creatively evaluates the convergence and divergence of filtering by residual characteristic factor, which is based on convergence and divergence trend. The parameters of the model are scaled and adjusted accordingly, so that the parameters of the model can be adapted adaptively and the filtering divergence can be prevented. Finally, the prediction model of tunnel vault settlement and surrounding displacement is established by using the proposed parameter adaptive extended Kalman filter method. Combined with a large number of field measurement data, the deformation prediction and analysis of the tunnel are carried out. By comparing the predicted results with the observed values, the performance and advantages of the digital adaptive extended Kalman filter for predicting the deformation in tunnel are analyzed, and its applicability is discussed. The prediction of deformation in tunnel is studied, which has higher theory and application value, and provides a new effective analysis method for the data processing of tunnel monitoring and measurement.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U456.31

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