在土木工程结构的长期健康监测过程中,变化环境对结构动力特性的影响甚至会掩盖损伤引起的动力特性变化,导致传统基于振动的损伤识别方法失效。独立成分分析(Independent Component Analysis, ICA)方法可用于环境因素影响分离,但其有效性受限于数据间需满足较好的线性相关性。为此,本文将切换温度引入到ICA方法中,提出适用于非线性环境因素影响下的切换ICA损伤识别方法。该方法结合主成分分析(Principal Component Analysis,PCA)和高斯混合模型(Gaussian Mixture Model,GMM)确定温度切换点,利用切换温度将非线性相关的频率数据分段线性化;然后,针对分段线性化后的频率数据,采用ICA方法计算数据的环境源和损伤源;最后,基于ICA损伤源,计算其SPE统计量作为损伤指标,通过X⁃bar控制图实现损伤预警。7自由度数值算例和Z24桥的监测数据验证了该方法的有效性。
Abstract
In the long-term health monitoring process of civil engineering structures, the influence of changing environments on the dynamic characteristics of the structure may even mask the changes in dynamic characteristics caused by damage, which renders traditional vibration-based damage identification methods ineffective. The independent component analysis (ICA) method can be used to separate the effects of environmental factors, however its effectiveness is limited by the need for highly linear correlation between data. To address this issue, this paper introduces switching temperature into the ICA method, and proposes a switching ICA damage identification method considering the influence of nonlinear environmental factors. This method combines principal component analysis (PCA) and Gaussian mixture model (GMM) to determine the temperature switching point, and uses the switching temperature to piecewise linearize the nonlinearly related frequency data; then, for the piecewise linearized frequency data, the ICA method is used to calculate the environmental source of the data and damage source; finally, based on the ICA damage source, the SPE statistic is calculated as the damage index, and early damage is detected through the X⁃bar control chart. A 7-degree-of-freedom numerical example and monitoring data of the Z24 bridge verify the effectiveness of the proposed method.
关键词
损伤识别 /
环境变化 /
独立成分分析 /
主成分分析 /
非线性相关
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Key words
damage identification /
environmental variations /
independent component analysis /
principal component analysis /
nonlinear correlation
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