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学者姓名：杨小梅
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Abstract ：
Modal frequencies are widely utilized as damage-sensitive features in vibration-based bridge damage warning. However, nonlinear and nonstationary modal variabilities induced by environmental fluctuations, compounded by improper selection of modal orders, may obscure damage sensitivity and degrade warning accuracy. Therefore, a subdomain cointegration (Sub-CI) method for environment-tolerant and early damage warning of bridges is proposed. The proposed method initially partitions the multimodal training data into local subsets that capture local variation information using the Dirichlet process K-means cluster. Subsequently, an implicit cointegration model with immunity to environmental effects within each cluster is established. Then, two types of damage indexes are considered separately, namely the robust Mahalanobis squared distance for multivariate cointegration and the Johnson transformation-based one-dimensional residual for bivariate cointegration. After that, the dynamic thresholds are derived from each Sub-CI model to assess the health status of in-service bridges. Results demonstrate that the proposed method mitigates modal variabilities induced by latent periodic temperature changes and overcomes larger damage prediction errors and higher false alarm rates in comparison to classical linear cointegration.
Keyword ：
Bridge structural health monitoring Bridge structural health monitoring cointegration analysis cointegration analysis early damage warning early damage warning environmental variabilities environmental variabilities modal frequency modal frequency
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| GB/T 7714 | Wang, Zhen , Liu, Guo-Hong , Yang, Xiao-Mei et al. Subdomain cointegration method for early damage warning of bridges considering nonlinear and nonstationary modal variabilities [J]. | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL , 2025 . |
| MLA | Wang, Zhen et al. "Subdomain cointegration method for early damage warning of bridges considering nonlinear and nonstationary modal variabilities" . | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2025) . |
| APA | Wang, Zhen , Liu, Guo-Hong , Yang, Xiao-Mei , Huang, Jie-Zhong , Yu, Feng , Xie, Hong-Lei et al. Subdomain cointegration method for early damage warning of bridges considering nonlinear and nonstationary modal variabilities . | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL , 2025 . |
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Abstract ：
Structural modal parameters are crucial for monitoring the condition of bridges. Operational modal analysis (OMA) has garnered great attention in vibration-based structural health monitoring of bridges because it only requires vibration measurements from multiple sensors. Slight asynchronization often occurs in these measurements during the monitoring process. Applying classical OMA methods, such as the natural excitation technique (NExT) combined with the eigensystem realization algorithm (ERA), to asynchronous vibration measurements can lead to significant errors in modal parameters. To address this issue, this study proposes a modal assurance criterion (MAC)-based time synchronization technique to generate reliable synchronous vibration measurements for modal identification. The MAC-based method takes advantage of the proportionality of modal components and is only capable of detecting nonsynchronized issues between single-degree-of-freedom (SDOF) signals. A variational mode extraction (VME) technique is employed to iteratively decompose bridge vibration measurements into SDOF components. The VME technique eliminates the need for artificially predefining the number of modes, which was required in many signal decomposition techniques. After time synchronization, the proposed method employs the NExT-ERA-based automatic OMA method for modal identification. The effectiveness of the proposed method is demonstrated using vibration measurements from both the finite element model of a highway bridge and field monitoring data from an actual bridge. The results show that the proposed method successfully synchronizes vibration signals and identifies mode shapes, even in the presence of modal node phenomena.
Keyword ：
asynchronous detection asynchronous detection bridge bridge health monitoring health monitoring modal analysis modal analysis signal decomposition signal decomposition
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| GB/T 7714 | Chen, Tao , Yang, Xiao-Mei , Yang, Shu-Han et al. Variational Mode Extraction-Guided Automated Asynchronous Operational Modal Analysis for Bridges [J]. | STRUCTURAL CONTROL & HEALTH MONITORING , 2025 , 2025 (1) . |
| MLA | Chen, Tao et al. "Variational Mode Extraction-Guided Automated Asynchronous Operational Modal Analysis for Bridges" . | STRUCTURAL CONTROL & HEALTH MONITORING 2025 . 1 (2025) . |
| APA | Chen, Tao , Yang, Xiao-Mei , Yang, Shu-Han , Yao, Xiao-Jun , Zheng, Yong-Xiang . Variational Mode Extraction-Guided Automated Asynchronous Operational Modal Analysis for Bridges . | STRUCTURAL CONTROL & HEALTH MONITORING , 2025 , 2025 (1) . |
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Abstract ：
针对准确定位土木工程结构突变损伤的损伤时刻和损伤位置问题,提出了基于变分模态分解(variational mode decomposition,VMD)与差分整合移动平均自回归(autoregressive integration moving average,ARIMA)模型的突变损伤识别方法.首先,利用自回归模型功率谱确定初始频率及需要分解的模态数量,接着通过VMD方法将振动非平稳信号初步分解为多个平稳的分量信号;然后,利用ARIMA模型来拟合各阶信号分量,获取模型残差,再利用ARIMA拟合模型信号分量得到的模型残差确定损伤的具体时刻;最后,利用主成分分析法获取结构的模态振型,构造一个基于频率与振型的损伤指标,结合损伤阈值定位出损伤位置.该方法通过地震激励下十自由度框架模拟算例以及实际简支钢桁梁桥数据进行分析.结果证实,该方法能够用于平稳及非平稳激励下的结构损伤时刻和损伤位置的定位.
Keyword ：
变分模态分解(VMD) 变分模态分解(VMD) 差分整合移动平均自回归(ARIMA)模型 差分整合移动平均自回归(ARIMA)模型 损伤识别 损伤识别 模型残差 模型残差 自回归模型功率谱 自回归模型功率谱
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| GB/T 7714 | 姚小俊 , 孙守鹏 , 王强 et al. 变分模态分解与时间序列模型相结合的结构损伤识别方法研究 [J]. | 振动与冲击 , 2025 , 44 (5) : 131-139,217 . |
| MLA | 姚小俊 et al. "变分模态分解与时间序列模型相结合的结构损伤识别方法研究" . | 振动与冲击 44 . 5 (2025) : 131-139,217 . |
| APA | 姚小俊 , 孙守鹏 , 王强 , 杨小梅 . 变分模态分解与时间序列模型相结合的结构损伤识别方法研究 . | 振动与冲击 , 2025 , 44 (5) , 131-139,217 . |
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Abstract ：
Long-span bridges are the key component of the human transportation system, linking communities over vast obstacles. To ensure the safe operation of long-span bridges, structural health monitoring (SHM) is perhaps the most effective solution. This study takes a 430m four-span continuous girder bridge as an example and systematically presents an implementation example of the SHM system on the bridge regarding monitoring items, sensor placement, and sensor parameters. The monitoring data including operational load and bridge response are analyzed and the statistical rules of these monitoring data are presented. Besides, the modal parameters of the girder are tracked from long-term vibration monitoring data. Furthermore, the correlations between structural temperature and bridge response are analyzed, and the correlation formulas between bridge modal frequency, static responses, and structural temperature are finally given. The analysis results reveal significant seasonal variations in the responses of the continuously monitored girder bridge, offering valuable insights for data-driven assessment and early warning systems for bridges.
Keyword ：
continuous girder bridge continuous girder bridge correlation analysis correlation analysis operational load operational load statistic analysis statistic analysis Structural health monitoring Structural health monitoring
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| GB/T 7714 | Yang, Xiao-Mei , Wang, Zhi-Wen , Zheng, Xu et al. Structural Health Monitoring of Long-Span Continuous Girder Bridge: System Implementation and Data Analysis [J]. | INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS , 2024 , 25 (01) . |
| MLA | Yang, Xiao-Mei et al. "Structural Health Monitoring of Long-Span Continuous Girder Bridge: System Implementation and Data Analysis" . | INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 25 . 01 (2024) . |
| APA | Yang, Xiao-Mei , Wang, Zhi-Wen , Zheng, Xu , Guan, Ze-Xin , Yang, Dong-Hui , Yi, Ting-Hua . Structural Health Monitoring of Long-Span Continuous Girder Bridge: System Implementation and Data Analysis . | INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS , 2024 , 25 (01) . |
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