为提高环境和运营变化(environmental and operational variations,EOV)影响下的桥梁损伤检测可靠性,结合逆非线性主成分分析(inverse nonlinear principal component analysis,INLPCA)和极值理论,提出一种新的桥梁损伤检测方法.该方法...为提高环境和运营变化(environmental and operational variations,EOV)影响下的桥梁损伤检测可靠性,结合逆非线性主成分分析(inverse nonlinear principal component analysis,INLPCA)和极值理论,提出一种新的桥梁损伤检测方法.该方法采用INLPCA对桥梁损伤特征进行建模,利用不完备健康监测数据的估计均方误差和添加神经网络训练惩罚项控制INLPCA的非线性程度.采用INLPCA对损伤特征的重构误差和马氏平方距离(Mahalanobis squared distance,MSD)建立损伤指标(ID),最后基于ID的广义极值(generalized extreme value,GEV)分布建立损伤检测阈值.以比利时KW51铁路桥和天津永和斜拉桥为例,验证所提方法的有效性.结果表明,所提方法能准确检测EOV影响下的桥梁损伤,且对不同桥型和不同损伤特征均有良好的适用性.展开更多
A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited...A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.展开更多
文摘A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.
