变环境下的桥梁模态参数分析

发布时间：2018-04-19 03:33

本文选题：模式识别 + 机器学习　；参考：《哈尔滨工业大学》2015年硕士论文

【摘要】：随着交通量的迅速增大,现有桥梁结构通常长期在超负荷流量情况下服役,同时还要遭受地震、风暴等自然灾害的侵袭,导致结构承载力下降,影响其运营安全。所以通过在结构上布设的实时监测系统,监测结构的动力特性,从而对结构进行损伤识别和状态评估,对于桥梁结构的运营安全具有重要意义。但大量的研究表明,这种方法应用于大型土木工程结构时,由于测量的噪声和运营状况的变化等,会使得识别出的模态参数产生相应的变化,当变化范围大于由于结构自身损伤而造成的模态参数变化时,很难对结构进行准确的损伤识别。一般情况下,造成模态参数变化的因素包括:1、环境条件(温度的变化、地基状态和湿度);2、运行条件(车辆荷载状况和其他外界激励);3、测试以及后处理存在的误差。本文首先介绍了在环境因素变化条件下结构模态参数研究的现状,然后基于环境激励下的模态参数识别技术:频域分解法(Frequency Domain Decomposition,FDD)和自然激励(Natural Excitation Technique,NEx T)与最小特征系统实现(Eigen-system Realization Algorithms,ERA)的联合识别法,对桥面加速度响应数据进行了分析,并且根据对监测到的应变数据进行分析,选取合适的特征向量和模式数目,从而对车辆荷载进行模式识别,提取轻车模式状态下的加速度监测数据和温湿度监测数据,识别出模态参数并计算出相应的温湿度特征值,构建样本集合;最后运用支持向量机,直接利用现场实测数据和识别出的模态参数值建立了数据驱动的特征频率预测模型,并利用该模型反过来对特征频率影响因素进行了研究分析。本文首次将模式识别和机器学习相结合的方法应用到桥梁结构在变环境条件下的模态参数研究分析中,直接利用实测数据建立了特征频率的预测模型,并且在利用该模型进行特征频率影响因素的研究分析中发现了一些新知识,为环境变化条件下结构模态参数的研究提供一种有效的方法。
[Abstract]:With the rapid increase of traffic volume, the existing bridge structures usually serve under the condition of overload and discharge for a long time. At the same time, they are also affected by natural disasters, such as earthquakes and storms, which lead to the decrease of the bearing capacity of the structure and affect its operation safety.Therefore, it is of great significance for the safety of the bridge structure to monitor the dynamic characteristics of the structure by the real-time monitoring system, so as to identify the damage and evaluate the state of the structure.However, a large number of studies have shown that, when this method is applied to large civil engineering structures, the measured noise and the change of operating conditions will cause the corresponding changes of the identified modal parameters.When the variation range is larger than the change of modal parameters caused by the damage of the structure itself, it is difficult to identify the damage of the structure accurately.In general, the factors causing modal parameter change include: 1, environmental conditions (temperature change, ground state and humidity), operating conditions (vehicle load and other external excitations), errors in testing and post-processing.This paper first introduces the research status of structural modal parameters under the change of environmental factors.Then, the acceleration response data of bridge deck are analyzed based on the modal parameter identification techniques under ambient excitation: frequency Domain decomposition (FDD) and Natural Excitation technique (NEX T) and minimum feature system implementation (Eigen-system Realization algorithm ERAA).Based on the analysis of the strain data, the appropriate eigenvector and the number of patterns are selected to identify the vehicle load, and the acceleration monitoring data and the temperature and humidity monitoring data are extracted.The modal parameters are identified, the corresponding temperature and humidity eigenvalues are calculated, and the sample set is constructed. Finally, the data-driven characteristic frequency prediction model is established by using the field measured data and the identified modal parameters directly by using the support vector machine.The influence factors of characteristic frequency are analyzed by using this model.In this paper, the method of pattern recognition and machine learning is first applied to the modal parameter analysis of bridge structure under the condition of variable environment, and the prediction model of characteristic frequency is established by using the measured data directly.Some new knowledge is found in the research and analysis of the influence factors of characteristic frequency by using this model, which provides an effective method for the study of structural modal parameters under the condition of environmental change.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U446

【参考文献】