基于自动模态提取和环境影响分离的桥梁结构性能变化识别
发布时间：2018-10-04 23:17
【摘要】：建立合理的健康监测系统,发现桥梁结构的早期结构整体性能变化对避免安全事故与过大的经济损失的发生具有重大意义。本文通过建立连续动态健康监测系统,获取桥梁结构的动态响应信号,基于自动模态参数提取方法获取大量动态参数,进而建立动态参数与环境因素的统计识别模型,去除环境因素对动态参数的影响,提取只对结构整体变化敏感的指标,识别结构整体性能变化。为了获取大量准确的模态参数,本文对连续动态数据运用协方差驱动的随机子空间方法(SSI-COV)基本理论和模糊聚类算法,得到不含虚假模态的稳定图,实现自动模态参数提取。通过运用多重线性回归方法,对大量的动态参数与相应环境因素建立统计模型。对最优统计学模型计算得到的残差矩阵进行离群值分析,得到只对结构性能变化敏感的新颖度指标,识别结构整体性能变化。本文选用适用于National Instrument的Lab VIEW软件环境,开发数据连续采集和自动模态参数提取程序。数据采集程序通过模块化的编译方式实现数据的连续采集、数据预处理和自动定时存储功能。自动模态参数提取程序运用SSI-COV理论获取稳定图,通过去除稳定图中的虚假模态,实现自动模态参数提取。通过滨州黄河大桥模型9 d的连续自动采集与识别,检验了自动采集程序和自动模态参数提取程序的适用性。基于深圳大沙河桥的数值模拟结果与模态实验结果,建立连续动态监测系统;通过42 d的自动连续采集与自动模态分析,得出施工阶段大沙河模态频率的变化,并对42 d结构频率与温度数据进行相关性分析,识别出施工阶段结构性能变化。运用葡萄牙Pedro e Inês桥的5年监测数据识别运营阶段结构性能变化。运用多重线性回归的方法建立第1年温度与结构频率之间的非线性关系。以此作为基准模型,去除环境因素对结构频率的影响,提取只对结构性能变化敏感的指标,得到5年内反映结构性能变化的新颖度指标的变化,识别结构早期性能变化。
[Abstract]:By establishing a reasonable health monitoring system, it is found that the change of the whole performance of the bridge structure in the early stage is of great significance to avoid the occurrence of safety accidents and excessive economic losses. In this paper, a continuous dynamic health monitoring system is established to obtain the dynamic response signals of the bridge structure, and a large number of dynamic parameters are obtained based on the automatic modal parameter extraction method, and then the statistical identification model of the dynamic parameters and environmental factors is established. Removing the influence of environmental factors on the dynamic parameters, extracting the index which is sensitive to the overall change of the structure, and identifying the overall performance change of the structure. In order to obtain a large number of accurate modal parameters, the basic theory of covariance-driven stochastic subspace method (SSI-COV) and fuzzy clustering algorithm are applied to the continuous dynamic data in this paper, and the stability graph with no false modal is obtained, and the automatic modal parameter extraction is realized. By using multiple linear regression method, a statistical model is established for a large number of dynamic parameters and corresponding environmental factors. The outlier value of the residual matrix calculated by the optimal statistical model is analyzed and a novel index which is only sensitive to the structural performance change is obtained to identify the overall performance change of the structure. In this paper, a Lab VIEW software environment suitable for National Instrument is used to develop a program for continuous data acquisition and automatic modal parameter extraction. The data acquisition program realizes continuous data acquisition, data preprocessing and automatic timing storage through modular compilation. The automatic modal parameter extraction program uses SSI-COV theory to obtain the stability diagram. By removing the false modal in the stability diagram, the automatic modal parameter extraction is realized. Through continuous automatic acquisition and identification of the model of Binzhou Yellow River Bridge for 9 days, the applicability of the automatic acquisition program and the automatic modal parameter extraction program are tested. Based on the numerical simulation results and modal experiment results of Dasha River Bridge in Shenzhen, a continuous dynamic monitoring system is established, and the modal frequency variation of Dasha River in construction stage is obtained by 42 days of automatic continuous acquisition and automatic modal analysis. The correlation analysis of 42 d structure frequency and temperature data is carried out to identify the structural performance changes in construction stage. The 5-year monitoring data of Pedro e In 锚 s bridge in Portugal are used to identify the structural performance changes in operation phase. The nonlinear relationship between the temperature of the first year and the frequency of the structure is established by the method of multiple linear regression. The model is used as a benchmark to remove the influence of environmental factors on the structural frequency and to extract the index which is only sensitive to the structural performance change. The variation of the novelty index reflecting the structural performance change within 5 years can be obtained and the early structural performance change can be identified.
【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】：2017
【分类号】：U446
本文编号：2252273
[Abstract]:By establishing a reasonable health monitoring system, it is found that the change of the whole performance of the bridge structure in the early stage is of great significance to avoid the occurrence of safety accidents and excessive economic losses. In this paper, a continuous dynamic health monitoring system is established to obtain the dynamic response signals of the bridge structure, and a large number of dynamic parameters are obtained based on the automatic modal parameter extraction method, and then the statistical identification model of the dynamic parameters and environmental factors is established. Removing the influence of environmental factors on the dynamic parameters, extracting the index which is sensitive to the overall change of the structure, and identifying the overall performance change of the structure. In order to obtain a large number of accurate modal parameters, the basic theory of covariance-driven stochastic subspace method (SSI-COV) and fuzzy clustering algorithm are applied to the continuous dynamic data in this paper, and the stability graph with no false modal is obtained, and the automatic modal parameter extraction is realized. By using multiple linear regression method, a statistical model is established for a large number of dynamic parameters and corresponding environmental factors. The outlier value of the residual matrix calculated by the optimal statistical model is analyzed and a novel index which is only sensitive to the structural performance change is obtained to identify the overall performance change of the structure. In this paper, a Lab VIEW software environment suitable for National Instrument is used to develop a program for continuous data acquisition and automatic modal parameter extraction. The data acquisition program realizes continuous data acquisition, data preprocessing and automatic timing storage through modular compilation. The automatic modal parameter extraction program uses SSI-COV theory to obtain the stability diagram. By removing the false modal in the stability diagram, the automatic modal parameter extraction is realized. Through continuous automatic acquisition and identification of the model of Binzhou Yellow River Bridge for 9 days, the applicability of the automatic acquisition program and the automatic modal parameter extraction program are tested. Based on the numerical simulation results and modal experiment results of Dasha River Bridge in Shenzhen, a continuous dynamic monitoring system is established, and the modal frequency variation of Dasha River in construction stage is obtained by 42 days of automatic continuous acquisition and automatic modal analysis. The correlation analysis of 42 d structure frequency and temperature data is carried out to identify the structural performance changes in construction stage. The 5-year monitoring data of Pedro e In 锚 s bridge in Portugal are used to identify the structural performance changes in operation phase. The nonlinear relationship between the temperature of the first year and the frequency of the structure is established by the method of multiple linear regression. The model is used as a benchmark to remove the influence of environmental factors on the structural frequency and to extract the index which is only sensitive to the structural performance change. The variation of the novelty index reflecting the structural performance change within 5 years can be obtained and the early structural performance change can be identified.
【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】：2017
【分类号】：U446
【参考文献】
相关期刊论文 前10条
1 徐飞鸿;朱检;张婷婷;;基于曲率模态曲线的结构损伤识别方法[J];世界地震工程;2015年04期
2 徐琪泽;吴金志;张毅刚;;基于振型相关性的结构模态参数频域自动识别[J];建筑结构;2015年05期
3 钟儒勉;宗周红;秦中远;郑沛娟;;基于多尺度模型修正的结合梁斜拉桥损伤识别方法[J];东南大学学报(自然科学版);2014年02期
4 吴向男;徐岳;梁鹏;李斌;;桥梁结构损伤识别研究现状与展望[J];长安大学学报(自然科学版);2013年06期
5 张纯;洪祖江;宋固全;;基于1范数正则化的模型修正方法在结构损伤识别中的应用[J];应用力学学报;2013年05期
6 宗周红;褚福鹏;牛杰;;基于响应面模型修正的桥梁结构损伤识别方法[J];土木工程学报;2013年02期
7 宗周红;牛杰;王浩;;基于模型确认的结构概率损伤识别方法研究进展[J];土木工程学报;2012年08期
8 熊红霞;刘沐宇;刘可文;;小波变换与SVD方法在结构损伤监测中的应用[J];公路;2009年03期
9 陈江;熊峰;;基于曲率模态振型的损伤识别方法研究[J];武汉理工大学学报;2007年03期
10 陈淮,禹丹江;基于曲率模态振型进行梁式桥损伤识别研究[J];公路交通科技;2004年10期
相关博士学位论文 前4条
1 刘宇飞;基于模型修正与图像处理的多尺度结构损伤识别[D];清华大学;2015年
2 孙磊;小波分析在桥梁健康监测中的应用研究[D];长安大学;2012年
3 章国稳;环境激励下结构模态参数自动识别与算法优化[D];重庆大学;2012年
4 李晶;基于广义柔度矩阵的结构损伤识别研究[D];吉林大学;2011年
相关硕士学位论文 前1条
1 洪祖江;基于正则化有限元模型修正方法的结构损伤识别[D];南昌大学;2013年
，本文编号：2252273
本文链接：https://www.wllwen.com/kejilunwen/daoluqiaoliang/2252273.html
