This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperatur...This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.展开更多
The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time vari...The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.展开更多
混凝土重力坝的自振频率包含结构整体和局部健康状态信息。基于自振频率来监控混凝土重力坝的健康状态,需要建立各种环境变量与各阶自振频率之间的复杂非线性映射关系,建模过程充满了不确定性。为此,该文通过亲和力传播(affinity propag...混凝土重力坝的自振频率包含结构整体和局部健康状态信息。基于自振频率来监控混凝土重力坝的健康状态,需要建立各种环境变量与各阶自振频率之间的复杂非线性映射关系,建模过程充满了不确定性。为此,该文通过亲和力传播(affinity propagation,AP)算法对模态稳定图进行聚类分析来实现模态参数的自动识别,通过环境量对自振频率影响规律的机理分析,并引入贝叶斯模型平均(Bayesian model averaging,BMA)技术来建立混凝土重力坝自振频率的安全监控模型,将模型自身不确定性考虑在内,可以自动平衡模型的复杂度与拟合程度,从而确定出对预测真正有贡献的输入变量。实际工程应用表明,采用基于BMA的混凝土重力坝自振频率安全监测模型,可以准确地模拟结构频率与环境量之间的映射关系,从而使总体模型具有更加准确的预测效果,能够很好地应用于混凝土重力坝安全性能监测,具有良好的工程应用前景。展开更多
基金National Natural Science Foundation of China Under Grant No.50725828 & No.50808041PhD Programs Foundation of Ministry of Education of China Under Grant No. 200802861011Scientific Research Foundation of Graduate School of Southeast University Under Grant No.YBJJ0923
文摘This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.
基金National Science Foundation Grant NSF CMS CAREER Under Grant No.9996290NSF CMMI Under Grant No.0830391
文摘The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.
文摘混凝土重力坝的自振频率包含结构整体和局部健康状态信息。基于自振频率来监控混凝土重力坝的健康状态,需要建立各种环境变量与各阶自振频率之间的复杂非线性映射关系,建模过程充满了不确定性。为此,该文通过亲和力传播(affinity propagation,AP)算法对模态稳定图进行聚类分析来实现模态参数的自动识别,通过环境量对自振频率影响规律的机理分析,并引入贝叶斯模型平均(Bayesian model averaging,BMA)技术来建立混凝土重力坝自振频率的安全监控模型,将模型自身不确定性考虑在内,可以自动平衡模型的复杂度与拟合程度,从而确定出对预测真正有贡献的输入变量。实际工程应用表明,采用基于BMA的混凝土重力坝自振频率安全监测模型,可以准确地模拟结构频率与环境量之间的映射关系,从而使总体模型具有更加准确的预测效果,能够很好地应用于混凝土重力坝安全性能监测,具有良好的工程应用前景。
