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【6h】Weighting Matrices and Model Order Determination in Stochastic System Identification for Civil Infrastructure Systems.

机译民用基础设施系统随机系统识别中的加权矩阵和模型阶确定。

【摘要】This research work consists in the development of a new robust stochastic system identification technique for the identification of dynamic characteristics of large-scale and complex civil infrastructure systems. Based on a stochastic state-space model framework, a new approach for state variable estimation is developed by proposing a new set of weighting matrices for the purpose of properly determining the order of a numerical model of the structure under consideration. For the development of such a new approach, a theoretical interpretation and investigation of the stochastic system identification process is provided within a general framework of the stochastic subspace method.;This work addresses four main topics of research: (1) to define existing stochastic subspace approaches in a unified framework that can be readily modified according to a desired performance. This unification is performed in a general multivariate analysis framework by using certain sets of weighting matrices; (2) to evaluate differences of the existing approaches in their performances for system identification. In this phase of research, after the investigation of the properties that each approach considers in its identification process of a dynamic system, the evaluation of their performance is conducted, in numerical and practical applications, with respect to the ability to highlight structural properties against noise ones in terms of the solution for singular value problem; (3) to develop a new robust stochastic subspace approach that properly discriminates structural modes, including both strongly and weakly excited modes, from noise ones in estimating state vectors from the singular value problem. Such a new approach is developed by proposing a new set of weighting matrices and its efficiency over the existing approaches is verified by using analytical, experimental and field data; and (4) to address frequently encountered problems in practical applications of a general subspace method. Considering such problems, complementary methods for the implementation of the subspace method are also investigated.

【摘要机译】这项研究工作包括开发一种新的鲁棒随机系统识别技术,用于识别大规模和复杂的民用基础设施系统的动态特征。基于随机状态空间模型框架,通过提出一组新的加权矩阵来开发状态变量估计的新方法,以正确确定所考虑结构的数值模型的顺序。为了开发这种新方法,在随机子空间方法的一般框架内提供了对随机系统识别过程的理论解释和研究。这项工作解决了四个主要的研究主题:(1)定义现有的随机子空间可以根据所需性能轻松修改的统一框架中的方法。通过使用某些加权矩阵集,可以在通用的多变量分析框架中执行这种统一; (2)评估现有方法在系统识别性能方面的差异。在此研究阶段,在研究了每种方法在动态系统识别过程中考虑的特性之后,在数值和实际应用中针对突出显示结构特性以抵抗噪声的能力进行了性能评估。关于奇异值问题的解决方案; (3)开发一种新的鲁棒随机子空间方法,该方法可以在从奇异值问题估计状态矢量时正确地区分结构模式(包括强激励模式和弱激励模式)与噪声模式。通过提出一组新的加权矩阵来开发这种新方法,并通过使用分析,实验和现场数据验证其在现有方法上的效率; (4)解决一般子空间方法在实际应用中经常遇到的问题。考虑到这些问题,还研究了用于实现子空间方法的补充方法。

【作者】Hong, Ah Lum.;

【作者单位】Columbia University.;

【年(卷),期】2010(),

【年度】2010

【页码】174 p.

【总页数】174

【原文格式】PDF

【正文语种】eng

【中图分类】;

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