基于概率统计和结构识别的多模型健康监测方法

发布时间：2018-05-07 19:12

本文选题：性能评估 + 子空间识别　；参考：《东南大学》2015年硕士论文

【摘要】：结构健康监测是了解、评估和预测结构运行状况的强大工具。本文研究如何利用健康监测技术进行结构的性能评估。对这个问题作了两种方法上的创新：首先开发了一种新的基于振动数据的结构柔度识别的新方法,即基于子空间识别理论的时域识别方法；其次,由于健康监测数据的各种不确定性的影响,用结构健康监测数据对初始建立的有限元模型进行矫正会时出现的多个有限元模型都可能与健康监测数据匹配,针对这一问题提出了基于概率的多模型方法框架进行解决。研究的主要的内容和结论如下：(1)利用基于时域的子空间方法(SI)进行结构的柔度识别。不像常用的随机子空间识别技术(SSI)只识别出结构系统的A和C,所提议方法可以识别完整的结构系统的状态矩阵A,B,C,D。利用完整的结构状态矩阵能够解耦的特性可以计算结构的模态参与系数,进而识别出结构的柔度。识别结构柔度后,可以预测结构在任意荷载下的位移响应,有替代卡车静载实验方法进行中小桥梁结构承载能力评估的潜力。(2)由于不确定性的影响,在模型修正中许多的模型都可以与健康监测数据相匹配。本文开发了一种基于概率方法的多模型框架理论来解决上述非唯一“最优”有限元模型问题。在此方法中,主要利用蒙特卡洛抽样(MC)或马尔科夫链蒙特卡洛(MCMC)抽样方法对结构关键参数进行抽样,形成多个有限元模型库并进行模型库的评估,依据多个有限元模型形成的模型库,而不是单一的“最优”有限元模型进行结构识别和性能评估。(3)分别利用三跨简支梁桥有限元模型和实验室6m简支梁作为例子,验证了所提议的结构柔度识别新方法的正确性及实用性,并讨论了基于柔度识别的结构性能评估方法。(4)利用一个四层框架基准结构实施并验证了基于概率的多模型方法,进行了结构基于概率的响应预测和可靠度评估。
[Abstract]:Structural health monitoring is a powerful tool for understanding, evaluating and predicting the performance of structures. This paper studies how to use health monitoring technology to evaluate the performance of the structure. First, a new method of structural compliance recognition based on vibration data is developed, which is based on the theory of subspace recognition in time domain. Because of the uncertainty of the health monitoring data, many finite element models that appear when the initial finite element model is corrected with the structural health monitoring data may match the health monitoring data. A probabilistic multi-model framework is proposed to solve this problem. The main contents and conclusions of the study are as follows: (1) the subspace method based on time domain is used to identify the structure flexibility. Unlike the commonly used stochastic subspace recognition technique (SSI), only A and C of the structural system can be identified. The proposed method can identify the complete state matrix of the structural system. The modal participation coefficient of the structure can be calculated by using the characteristic that the complete structure state matrix can be decoupled and the flexibility of the structure can be identified. After recognizing the flexibility of the structure, the displacement response of the structure under arbitrary load can be predicted, and there is the potential to evaluate the bearing capacity of small and medium-sized bridge structures instead of the truck static load test method. In model modification, many models can be matched with health monitoring data. In this paper, a multi-model framework theory based on probabilistic method is developed to solve the above non-unique "optimal" finite element model problem. In this method, we mainly use Monte Carlo sampling (MC) or Markov chain Monte Carlo (MCMC) sampling method to sample the key parameters of the structure to form several finite element model bases and evaluate the model base. Based on the model base formed by multiple finite element models, instead of a single "optimal" finite element model, structural identification and performance evaluation are carried out, respectively, using the finite element model of three-span simply supported beam bridge and the 6m simple supported beam in laboratory as examples. The correctness and practicability of the proposed new method for structural compliance recognition are verified, and the structure performance evaluation method based on compliance recognition is discussed. The method is implemented and validated by using a four-layer frame benchmark structure to validate the probabilistic multi-model method. The probabilistic response prediction and reliability evaluation are carried out.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.12

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