基于无线传感器网络的斜拉桥模型动力特性分析

发布时间：2018-07-27 10:18

【摘要】：桥梁结构在正常使用阶段及突发事件中承受动力荷载作用，其动力特性分析是桥梁结构设计、施工控制及运营阶段动力监测的基础分析。本文依托滨州黄河公路大桥缩尺模型，以有线数据采集系统识别的动力特性参数为基准，开展了基于无线传感器网络识别的动力特性参数进行斜拉桥模型动力特性分析的研究工作，为无线传感器网络在桥梁结构动力特性分析方面实际应用做一些基础性研究。首先，结合本实验室现有的实验仪器条件，确立了试验方案并建立了振动试验系统——激振系统、采集系统和分析系统。试验方案采用多点激励多点响应试验模态分析方法，，采用跑点——参考点的分组测量法。本文激振系统和有线采集系统使用了虚拟仪器，其系统开发使用了LabVIEW8.6软件平台；无线采集系统传感器节点使用了Imote2硬件平台和ISM400传感板；分析系统采用MATLAB软件进行数据处理。其次，深入研究了模态试验分析和有限单元法两类参数识别方法。掌握了特征系统实现算法和频域分解法识别动力特性参数的原理。基于振动试验有线采集系统获得的加速度信号，使用MATLAB软件自行编写的特征系统实现算法和频域分解法程序识别缩尺模型动力特性参数（桥面板前6阶竖向频率和振型）；对比了两种方法识别的频率和归一化振型，验证了两种算法程序正确。本文选用双主梁建模方法建立了实验室缩尺斜拉桥模型的有限元模型，并对其进行了模态分析，提取了桥面板前6阶竖向振型和频率。最后，使用上述模态参数识别方法对无线采集系统的加速度数据进行了参数识别，提取的模态参数与有线结果基本一致，表明基于Imote2节点的无线传感器网络能够用于模型桥的动力特性分析。以振型特征一致为准则，频率较大误差为竖向第1阶3.41%和竖向第5阶2.26%，其余均在1%以下。分析了模型桥动力特性：模态比较密集，符合斜拉桥特性；桥面板竖向刚度相对小于横向刚度；扭弯频率比为2.77。
[Abstract]:The dynamic characteristic analysis of bridge structure is the basic analysis of bridge structure design, construction control and dynamic monitoring in operation stage. Based on the scaling model of Binzhou Yellow River Highway Bridge and the dynamic characteristic parameters identified by the wired data acquisition system, The dynamic characteristic analysis of cable-stayed bridge model based on the dynamic characteristic parameters identified by wireless sensor network is carried out, which makes some basic research for the practical application of wireless sensor network in the dynamic characteristics analysis of bridge structure. First of all, according to the existing experimental equipment conditions in our laboratory, the test scheme is established, and the vibration test system-excitation system, the collection system and the analysis system are established. The test scheme adopts multi-point excitation and multi-point response test modal analysis method, and adopts the group measurement method of running point and reference point. In this paper, the excitation system and the wired acquisition system use virtual instrument, the system development uses LabVIEW8.6 software platform; the wireless acquisition system sensor nodes use Imote2 hardware platform and ISM400 sensor board; the analysis system uses MATLAB software for data processing. Secondly, two kinds of parameter identification methods, modal test analysis and finite element method, are studied. The principle of feature system realization algorithm and frequency domain decomposition method to identify dynamic characteristic parameters is mastered. Based on the acceleration signal obtained from the vibration test wired acquisition system, the algorithm of characteristic system written by MATLAB software and the frequency domain decomposition program are used to identify the dynamic characteristic parameters of the scale model (the first 6 vertical frequencies and modes of the bridge deck). The frequency and the normalized mode of the two methods are compared, and the two algorithms are proved to be correct. In this paper, the finite element model of the laboratory scale cable-stayed bridge is established by using the two-beam modeling method, and the modal analysis is carried out, and the first six vertical modes and frequencies of the deck plate are extracted. Finally, the acceleration data of the wireless acquisition system are identified by using the above modal parameter identification method, and the obtained modal parameters are basically consistent with the wired results. It shows that the wireless sensor network based on Imote2 node can be used to analyze the dynamic characteristics of the model bridge. The frequency error is 3.41% for vertical first order and 2.26% for vertical fifth order, and the rest are below 1%. The dynamic characteristics of the model bridge are analyzed as follows: the modal is dense and accords with the characteristics of the cable-stayed bridge, the vertical stiffness of the deck is relatively smaller than the transverse stiffness, and the ratio of torsional and bending frequency is 2.77.
【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】：2012
【分类号】：U448.27

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