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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods
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作者 Joao Pedro SANTOS Christian CREMONA +2 位作者 andre d. orcesi Paulo SILVEIRA Luis CALAdO 《Frontiers of Structural and Civil Engineering》 CSCD 2015年第1期1-16,共16页
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
关键词 structural health monitoring early-damage detection principal component analysis symbolic data symbolic dissimilarity measures cluster analysis numerical model damage simulations
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